Introduction

This markdown imports the processed data and conducts the exploratory data analysis. Follow the processing markdowns in the processing_code folder to generate the processed data.


This analysis examines the predictors of allocation of FEMA funds during disaster declarations. In terms of the datasets, the two outcomes of interest are Requested Amount (ReqAmt) and Obligated Amount (OblAmt).


For each variable, the following steps will be completed:

  1. Produce and print some numerical output (e.g. table, summary statistics)
  2. Create a histogram or density plot (continuous variables only)
  3. Scatterplot, boxplot, or other similar plots against the main outcome of interest
  4. Any other exploration steps that may be useful.

The steps will be numbered for each variable as specified above.


Required Packages

The following R packages are required for this script:


Load Processed Data

Load the data generated from the markdowns in the processing_code folder.

#define file path
data_location <- here::here("data", "processed_data", "analysisdata.rds")

#load data
EDAdata <- readRDS(data_location)

Data Overview

To better understand the data, let’s use summarytools to better visualize the data.

summarytools::dfSummary(EDAdata)
## Data Frame Summary  
## EDAdata  
## Dimensions: 326 x 25  
## Duplicates: 0  
## 
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------------
## No   Variable            Label                                    Stats / Values                     Freqs (% of Valid)    Graph                 Valid      Missing  
## ---- ------------------- ---------------------------------------- ---------------------------------- --------------------- --------------------- ---------- ---------
## 1    name                State Name                               1. California                       19 ( 5.8%)           I                     326        0        
##      [character]                                                  2. Texas                            17 ( 5.2%)           I                     (100.0%)   (0.0%)   
##                                                                   3. Louisiana                        16 ( 4.9%)                                                     
##                                                                   4. Florida                          13 ( 4.0%)                                                     
##                                                                   5. Washington                       11 ( 3.4%)                                                     
##                                                                   6. Minnesota                        10 ( 3.1%)                                                     
##                                                                   7. Oklahoma                         10 ( 3.1%)                                                     
##                                                                   8. South Dakota                     10 ( 3.1%)                                                     
##                                                                   9. West Virginia                    10 ( 3.1%)                                                     
##                                                                   10. North Carolina                   9 ( 2.8%)                                                     
##                                                                   [ 41 others ]                      201 (61.7%)           IIIIIIIIIIII                              
## 
## 2    state               State Abbreviation                       1. CA                               19 ( 5.8%)           I                     326        0        
##      [character]                                                  2. TX                               17 ( 5.2%)           I                     (100.0%)   (0.0%)   
##                                                                   3. LA                               16 ( 4.9%)                                                     
##                                                                   4. FL                               13 ( 4.0%)                                                     
##                                                                   5. WA                               11 ( 3.4%)                                                     
##                                                                   6. MN                               10 ( 3.1%)                                                     
##                                                                   7. OK                               10 ( 3.1%)                                                     
##                                                                   8. SD                               10 ( 3.1%)                                                     
##                                                                   9. WV                               10 ( 3.1%)                                                     
##                                                                   10. NC                               9 ( 2.8%)                                                     
##                                                                   [ 41 others ]                      201 (61.7%)           IIIIIIIIIIII                              
## 
## 3    latitude            Centroid Latitude of State               Mean (sd) : 37.8 (7.2)             51 distinct values          : . :           326        0        
##      [numeric]                                                    min < med < max:                                               : : :           (100.0%)   (0.0%)   
##                                                                   18.2 < 38 < 63.6                                               : : :                               
##                                                                   IQR (CV) : 9.4 (0.2)                                         . : : : .                             
##                                                                                                                            :   : : : : :                             
## 
## 4    longitude           Centroid Longitude of State              Mean (sd) : -93.7 (18.4)           44 distinct values                  : :     326        0        
##      [numeric]                                                    min < med < max:                                                     : : :     (100.0%)   (0.0%)   
##                                                                   -155.7 < -91.8 < -66.6                                               : : : :                       
##                                                                   IQR (CV) : 18.7 (-0.2)                                           .   : : : :                       
##                                                                                                                            .     : : : : : : :                       
## 
## 5    Population          State Population                         Mean (sd) : 9313643 (10333562)     51 distinct values    :                     326        0        
##      [numeric]                                                    min < med < max:                                         :                     (100.0%)   (0.0%)   
##                                                                   576851 < 5706494 < 39538223                              : .                                       
##                                                                   IQR (CV) : 7427864 (1.1)                                 : : .                                     
##                                                                                                                            : : :   . .   .                           
## 
## 6    Counties            Counties per State                       Mean (sd) : 75.3 (53.5)            46 distinct values        :                 326        0        
##      [integer]                                                    min < med < max:                                             : .               (100.0%)   (0.0%)   
##                                                                   1 < 67 < 254                                                 : :                                   
##                                                                   IQR (CV) : 42 (0.7)                                      : . : :                                   
##                                                                                                                            : : : : . . .     :                       
## 
## 7    FEMARegion          FEMA Region                              1. Region I                        27 ( 8.3%)            I                     326        0        
##      [character]                                                  2. Region II                       19 ( 5.8%)            I                     (100.0%)   (0.0%)   
##                                                                   3. Region III                      25 ( 7.7%)            I                                         
##                                                                   4. Region IV                       62 (19.0%)            III                                       
##                                                                   5. Region IX                       31 ( 9.5%)            I                                         
##                                                                   6. Region V                        37 (11.3%)            II                                        
##                                                                   7. Region VI                       54 (16.6%)            III                                       
##                                                                   8. Region VII                      20 ( 6.1%)            I                                         
##                                                                   9. Region VIII                     24 ( 7.4%)            I                                         
##                                                                   10. Region X                       27 ( 8.3%)            I                                         
## 
## 8    disasterNumber      Declaration Number                       Mean (sd) : 4157.6 (392.8)         326 distinct values                 :   :   326        0        
##      [integer]                                                    min < med < max:                                         .           . : : :   (100.0%)   (0.0%)   
##                                                                   3346 < 4276.5 < 4619                                     :           : : : :                       
##                                                                   IQR (CV) : 377 (0.1)                                     : .       : : : : :                       
##                                                                                                                            : :       : : : : :                       
## 
## 9    declarationType     Declaration Type                         1. DR                              261 (80.1%)           IIIIIIIIIIIIIIII      326        0        
##      [character]                                                  2. EM                               65 (19.9%)           III                   (100.0%)   (0.0%)   
## 
## 10   declarationTitle    Declaration Title                        1. COVID-19 PANDEMIC                52 (16.0%)           III                   326        0        
##      [character]                                                  2. SEVERE STORMS AND FLOODIN        31 ( 9.5%)           I                     (100.0%)   (0.0%)   
##                                                                   3. SEVERE STORMS, TORNADOES,        24 ( 7.4%)           I                                         
##                                                                   4. HURRICANE SANDY                  11 ( 3.4%)                                                     
##                                                                   5. WILDFIRES                        11 ( 3.4%)                                                     
##                                                                   6. SEVERE STORMS, FLOODING,         10 ( 3.1%)                                                     
##                                                                   7. HURRICANE MATTHEW                 9 ( 2.8%)                                                     
##                                                                   8. COVID-19                          8 ( 2.5%)                                                     
##                                                                   9. SEVERE STORMS, TORNADOES,         8 ( 2.5%)                                                     
##                                                                   10. SEVERE STORM AND FLOODING        6 ( 1.8%)                                                     
##                                                                   [ 90 others ]                      156 (47.9%)           IIIIIIIII                                 
## 
## 11   incidentType        Incident Type                            1. Biological                      60 (18.4%)            III                   326        0        
##      [character]                                                  2. Earthquake                       4 ( 1.2%)                                  (100.0%)   (0.0%)   
##                                                                   3. Fire                            20 ( 6.1%)            I                                         
##                                                                   4. Flood                           61 (18.7%)            III                                       
##                                                                   5. Hurricane                       73 (22.4%)            IIII                                      
##                                                                   6. Other                           15 ( 4.6%)                                                      
##                                                                   7. Severe Ice Storm                 4 ( 1.2%)                                                      
##                                                                   8. Severe Storm(s)                 82 (25.2%)            IIIII                                     
##                                                                   9. Tornado                          7 ( 2.1%)                                                      
## 
## 12   IncidentYear        Incident Year                            Mean (sd) : 2017 (2.9)             2012 : 33 (10.1%)     II                    326        0        
##      [numeric]                                                    min < med < max:                   2013 : 29 ( 8.9%)     I                     (100.0%)   (0.0%)   
##                                                                   2012 < 2017 < 2021                 2014 : 17 ( 5.2%)     I                                         
##                                                                   IQR (CV) : 5 (0)                   2015 : 28 ( 8.6%)     I                                         
##                                                                                                      2016 : 23 ( 7.1%)     I                                         
##                                                                                                      2017 : 36 (11.0%)     II                                        
##                                                                                                      2018 : 32 ( 9.8%)     I                                         
##                                                                                                      2019 : 28 ( 8.6%)     I                                         
##                                                                                                      2020 : 86 (26.4%)     IIIII                                     
##                                                                                                      2021 : 14 ( 4.3%)                                               
## 
## 13   IncidentMonth       Declaration Month                        1. January                         71 (21.8%)            IIII                  326        0        
##      [ordered, factor]                                            2. February                        22 ( 6.7%)            I                     (100.0%)   (0.0%)   
##                                                                   3. March                           22 ( 6.7%)            I                                         
##                                                                   4. April                           24 ( 7.4%)            I                                         
##                                                                   5. May                             22 ( 6.7%)            I                                         
##                                                                   6. June                            25 ( 7.7%)            I                                         
##                                                                   7. July                            13 ( 4.0%)                                                      
##                                                                   8. August                          32 ( 9.8%)            I                                         
##                                                                   9. September                       31 ( 9.5%)            I                                         
##                                                                   10. October                        41 (12.6%)            II                                        
##                                                                   [ 2 others ]                       23 ( 7.1%)            I                                         
## 
## 14   IncidentDuration    Incident Duration                        Mean (sd) : 135.7 (250.3)          67 distinct values    :                     326        0        
##      [numeric]                                                    min < med < max:                                         :                     (100.0%)   (0.0%)   
##                                                                   0 < 14 < 660                                             :                                         
##                                                                   IQR (CV) : 41.2 (1.8)                                    :                                         
##                                                                                                                            :                 :                       
## 
## 15   ResponseDays        Response Duration                        Mean (sd) : 1404.1 (898.4)         229 distinct values     :                   326        0        
##      [numeric]                                                    min < med < max:                                           :                   (100.0%)   (0.0%)   
##                                                                   70 < 1178.5 < 3588                                       . : . .                                   
##                                                                   IQR (CV) : 1406.5 (0.6)                                  : : : : : .                               
##                                                                                                                            : : : : : : :                             
## 
## 16   IH                  Individuals/Households Program Awarded   1. Not Awarded                     163 (50.0%)           IIIIIIIIII            326        0        
##      [factor]                                                     2. Awarded                         163 (50.0%)           IIIIIIIIII            (100.0%)   (0.0%)   
## 
## 17   IH_pct              Percent of Counties Awarded IH           Mean (sd) : 0.3 (1.6)              136 distinct values   :                     326        0        
##      [numeric]                                                    min < med < max:                                         :                     (100.0%)   (0.0%)   
##                                                                   0 < 0 < 27.7                                             :                                         
##                                                                   IQR (CV) : 0.2 (4.7)                                     :                                         
##                                                                                                                            :                                         
## 
## 18   PA                  Public Assistance Program Awarded        1. Not Awarded                       8 ( 2.5%)                                 326        0        
##      [factor]                                                     2. Awarded                         318 (97.5%)           IIIIIIIIIIIIIIIIIII   (100.0%)   (0.0%)   
## 
## 19   PA_pct              Percent of Counties Awarded PA           Mean (sd) : 0.6 (1.6)              228 distinct values   :                     326        0        
##      [numeric]                                                    min < med < max:                                         :                     (100.0%)   (0.0%)   
##                                                                   0 < 0.4 < 27.3                                           :                                         
##                                                                   IQR (CV) : 0.9 (2.6)                                     :                                         
##                                                                                                                            :                                         
## 
## 20   HM                  Hazard Mitigation Program                1. Not Awarded                      66 (20.2%)           IIII                  326        0        
##      [factor]                                                     2. Awarded                         260 (79.8%)           IIIIIIIIIIIIIII       (100.0%)   (0.0%)   
## 
## 21   HM_pct              Percent of Counties Awarded HM           Mean (sd) : 0.5 (1.6)              211 distinct values   :                     326        0        
##      [numeric]                                                    min < med < max:                                         :                     (100.0%)   (0.0%)   
##                                                                   0 < 0.2 < 27.2                                           :                                         
##                                                                   IQR (CV) : 0.6 (3.3)                                     :                                         
##                                                                                                                            :                                         
## 
## 22   TotalAgencies       Federal Agencies Involved                Mean (sd) : 6.2 (7)                30 distinct values    :                     326        0        
##      [numeric]                                                    min < med < max:                                         :                     (100.0%)   (0.0%)   
##                                                                   1 < 3 < 55                                               :                                         
##                                                                   IQR (CV) : 6 (1.1)                                       :                                         
##                                                                                                                            : : : .                                   
## 
## 23   FEMACSAvg           Average FEMA Cost Share                  Mean (sd) : 1 (0.1)                128 distinct values               :         326        0        
##      [numeric]                                                    min < med < max:                                                     :         (100.0%)   (0.0%)   
##                                                                   0.7 < 1 < 1                                                          :                             
##                                                                   IQR (CV) : 0.1 (0.1)                                                 :                             
##                                                                                                                                    . : :                             
## 
## 24   ReqAmt              Requested FEMA Funds                     Mean (sd) : 40601806 (255138973)   326 distinct values   :                     326        0        
##      [numeric]                                                    min < med < max:                                         :                     (100.0%)   (0.0%)   
##                                                                   0 < 420063.9 < 4388540486                                :                                         
##                                                                   IQR (CV) : 4700581 (6.3)                                 :                                         
##                                                                                                                            :                                         
## 
## 25   OblAmt              Obligated FEMA Funds                     Mean (sd) : 40698151 (256181278)   326 distinct values   :                     326        0        
##      [numeric]                                                    min < med < max:                                         :                     (100.0%)   (0.0%)   
##                                                                   0 < 434188.5 < 4408424297                                :                                         
##                                                                   IQR (CV) : 4625581 (6.3)                                 :                                         
##                                                                                                                            :                                         
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------------
skimr::skim(EDAdata)
Data summary
Name EDAdata
Number of rows 326
Number of columns 25
_______________________
Column type frequency:
character 6
factor 4
numeric 15
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
name 0 1 4 20 0 51 0
state 0 1 2 2 0 51 0
FEMARegion 0 1 8 11 0 10 0
declarationType 0 1 2 2 0 2 0
declarationTitle 0 1 8 81 0 100 0
incidentType 0 1 4 16 0 9 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
IncidentMonth 0 1 TRUE 12 Jan: 71, Oct: 41, Aug: 32, Sep: 31
IH 0 1 FALSE 2 Not: 163, Awa: 163
PA 0 1 FALSE 2 Awa: 318, Not: 8
HM 0 1 FALSE 2 Awa: 260, Not: 66

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
latitude 0 1 37.82 7.19 18.22 33.84 37.96 43.19 63.59 ▁▆▇▁▁
longitude 0 1 -93.67 18.41 -155.67 -99.90 -91.83 -81.16 -66.59 ▁▁▂▇▇
Population 0 1 9313643.01 10333562.39 576851.00 3011524.00 5706494.00 10439388.00 39538223.00 ▇▂▁▁▁
Counties 0 1 75.26 53.47 1.00 46.00 67.00 88.00 254.00 ▃▇▁▁▁
disasterNumber 0 1 4157.55 392.84 3346.00 4080.25 4276.50 4457.25 4619.00 ▅▁▂▇▇
IncidentYear 0 1 2016.97 2.89 2012.00 2015.00 2017.00 2020.00 2021.00 ▅▃▅▅▇
IncidentDuration 0 1 135.66 250.30 0.00 5.00 14.00 46.25 660.00 ▇▁▁▁▂
ResponseDays 0 1 1404.07 898.44 70.04 660.00 1178.54 2066.51 3588.00 ▇▅▅▃▂
IH_pct 0 1 0.34 1.59 0.00 0.00 0.00 0.24 27.69 ▇▁▁▁▁
PA_pct 0 1 0.59 1.56 0.00 0.14 0.36 1.00 27.31 ▇▁▁▁▁
HM_pct 0 1 0.48 1.56 0.00 0.04 0.21 0.60 27.25 ▇▁▁▁▁
TotalAgencies 0 1 6.23 7.03 1.00 2.00 3.00 8.00 55.00 ▇▂▁▁▁
FEMACSAvg 0 1 0.96 0.06 0.68 0.95 1.00 1.00 1.00 ▁▁▁▂▇
ReqAmt 0 1 40601806.50 255138972.55 0.00 51024.33 420063.94 4751605.15 4388540486.39 ▇▁▁▁▁
OblAmt 0 1 40698150.73 256181277.85 0.00 51024.33 434188.48 4676605.15 4408424297.39 ▇▁▁▁▁

Looking at the output of summarytools::dfsummary()

Before moving through the entire EDA, I want to create a secondary dataset with the outliers removed. This is the data actually used in the ML analysis, so this is the dataset that should be used for the EDA.

EDAdata_outliers <- EDAdata %>%
                      dplyr::filter(log(ReqAmt) > 0)

Let’s dive a little deeper into each variable.


Outcome of Interest: Requested FEMA Funds

The first of two primary outcomes of interest is the total amount of funds the state requested from the federal government for a disaster declaration.

#summary statistics
base::summary(EDAdata_outliers$ReqAmt)
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
##         78      52294     443989   40978913    4987600 4388540486
#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = ReqAmt)) +
  geom_density()

#try a log transformation out of curiosity 
ggplot2::ggplot(data = EDAdata_outliers, aes(x = log(ReqAmt))) +
  geom_density()

#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = ReqAmt)) +
  geom_boxplot()

#crop to $1M to get a better idea
ggplot2::ggplot(data = EDAdata_outliers, aes(y = ReqAmt)) +
  geom_boxplot()

#log transformed boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = log(ReqAmt))) +
  geom_boxplot()

##QQ plot
car::qqPlot(EDAdata_outliers$ReqAmt)

## [1] 233 192

These outputs show the requested amounts do not follow a normal distribution, which is to be expected given the broad range of disasters. We may need to remove outliers in the analysis, but for now, we’ll leave them to capture the true nature of disaster declarations. The largest outlier is likely Hurricane Maria. The log transformation does a better job of showing the distribution of the data, so we may need to consider an exponential model in the analysis. The transformed and untransformed figures suggest there are significant tails in the data.


Outcome of Interest: Obligated FEMA Funds

The alternative outcome of interest is the funding obligated from FEMA. While this may be the same as ReqAmt (especially for large-scale disasters), FEMA doesn’t always pay the amount the states request.

#summary statistics
base::summary(EDAdata_outliers$OblAmt)
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
##         78      52294     464902   41076152    4937600 4408424297
#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = OblAmt)) +
  geom_density()

#try a log transformation out of curiosity 
ggplot2::ggplot(data = EDAdata_outliers, aes(x = log(OblAmt))) +
  geom_density()

#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = OblAmt)) +
  geom_boxplot()

#crop to $1M to get a better idea
ggplot2::ggplot(data = EDAdata_outliers, aes(y = OblAmt)) +
  geom_boxplot()

#log transformed boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = log(OblAmt))) +
  geom_boxplot()

##QQ plot
car::qqPlot(EDAdata_outliers$OblAmt)

## [1] 233 192

It appears that obligated funds and requested funds have similar distributions and characteristics. The same commentary about the requested funds analysis applies here as well.

Before moving on, I want to create a plot of Requested vs obligated Funds to understand the relationship between the two outcomes:

#including all points
funds_comp <- EDAdata_outliers %>%
                ggplot2::ggplot(aes(x = log(ReqAmt), y = log(OblAmt))) +
                         geom_smooth(color = "#440154",
                                     size = 2) +
                         geom_point(color = "#1f9e89",
                                    alpha = 0.5) +
                         scale_y_continuous(expand = c(0,0),
                                            limits = c(0, NA)) +
                         scale_x_continuous(expand = c(0,0),
                                            limits = c(0, NA)) +
                         labs(x = "log(Requested Funding)",
                              y = "log(Obligated Funding)")

funds_comp
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

#save file
funds_comp_fig_file = here("results","funds_comp.png")
ggsave(filename = funds_comp_fig_file, plot = funds_comp)
## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

I also want a density plot of both log-transformed outcomes on one plot, so that can go here as well.

#convert to long format in order to be able to plot both outcomes with same variable
data_long <- gather(EDAdata_outliers, FundType, funds, ReqAmt:OblAmt, factor_key=TRUE)
## Warning: attributes are not identical across measure variables;
## they will be dropped
data_long
##                     name state latitude  longitude Population Counties
## 1                 Alaska    AK 63.58875 -154.49306     733391       34
## 2                 Alaska    AK 63.58875 -154.49306     733391       34
## 3                 Alaska    AK 63.58875 -154.49306     733391       34
## 4                 Alaska    AK 63.58875 -154.49306     733391       34
## 5                Alabama    AL 32.31823  -86.90230    5024279       67
## 6                Alabama    AL 32.31823  -86.90230    5024279       67
## 7                Alabama    AL 32.31823  -86.90230    5024279       67
## 8                Alabama    AL 32.31823  -86.90230    5024279       67
## 9               Arkansas    AR 35.20105  -91.83183    3011524       75
## 10              Arkansas    AR 35.20105  -91.83183    3011524       75
## 11              Arkansas    AR 35.20105  -91.83183    3011524       75
## 12              Arkansas    AR 35.20105  -91.83183    3011524       75
## 13              Arkansas    AR 35.20105  -91.83183    3011524       75
## 14              Arkansas    AR 35.20105  -91.83183    3011524       75
## 15              Arkansas    AR 35.20105  -91.83183    3011524       75
## 16              Arkansas    AR 35.20105  -91.83183    3011524       75
## 17               Arizona    AZ 34.04893 -111.09373    7151502       15
## 18               Arizona    AZ 34.04893 -111.09373    7151502       15
## 19               Arizona    AZ 34.04893 -111.09373    7151502       15
## 20            California    CA 36.77826 -119.41793   39538223       58
## 21            California    CA 36.77826 -119.41793   39538223       58
## 22            California    CA 36.77826 -119.41793   39538223       58
## 23            California    CA 36.77826 -119.41793   39538223       58
## 24            California    CA 36.77826 -119.41793   39538223       58
## 25            California    CA 36.77826 -119.41793   39538223       58
## 26            California    CA 36.77826 -119.41793   39538223       58
## 27            California    CA 36.77826 -119.41793   39538223       58
## 28            California    CA 36.77826 -119.41793   39538223       58
## 29            California    CA 36.77826 -119.41793   39538223       58
## 30            California    CA 36.77826 -119.41793   39538223       58
## 31            California    CA 36.77826 -119.41793   39538223       58
## 32            California    CA 36.77826 -119.41793   39538223       58
## 33            California    CA 36.77826 -119.41793   39538223       58
## 34            California    CA 36.77826 -119.41793   39538223       58
## 35            California    CA 36.77826 -119.41793   39538223       58
## 36            California    CA 36.77826 -119.41793   39538223       58
## 37            California    CA 36.77826 -119.41793   39538223       58
## 38            California    CA 36.77826 -119.41793   39538223       58
## 39              Colorado    CO 39.55005 -105.78207    5773714       64
## 40              Colorado    CO 39.55005 -105.78207    5773714       64
## 41              Colorado    CO 39.55005 -105.78207    5773714       64
## 42              Colorado    CO 39.55005 -105.78207    5773714       64
## 43              Colorado    CO 39.55005 -105.78207    5773714       64
## 44           Connecticut    CT 41.60322  -73.08775    3605944        8
## 45           Connecticut    CT 41.60322  -73.08775    3605944        8
## 46           Connecticut    CT 41.60322  -73.08775    3605944        8
## 47           Connecticut    CT 41.60322  -73.08775    3605944        8
## 48           Connecticut    CT 41.60322  -73.08775    3605944        8
## 49           Connecticut    CT 41.60322  -73.08775    3605944        8
## 50  District of Columbia    DC 38.90599  -77.03342     689545        1
## 51  District of Columbia    DC 38.90599  -77.03342     689545        1
## 52              Delaware    DE 38.91083  -75.52767     989948        3
## 53               Florida    FL 27.66483  -81.51575   21538187       67
## 54               Florida    FL 27.66483  -81.51575   21538187       67
## 55               Florida    FL 27.66483  -81.51575   21538187       67
## 56               Florida    FL 27.66483  -81.51575   21538187       67
## 57               Florida    FL 27.66483  -81.51575   21538187       67
## 58               Florida    FL 27.66483  -81.51575   21538187       67
## 59               Florida    FL 27.66483  -81.51575   21538187       67
## 60               Florida    FL 27.66483  -81.51575   21538187       67
## 61               Florida    FL 27.66483  -81.51575   21538187       67
## 62               Florida    FL 27.66483  -81.51575   21538187       67
## 63               Florida    FL 27.66483  -81.51575   21538187       67
## 64               Florida    FL 27.66483  -81.51575   21538187       67
## 65               Florida    FL 27.66483  -81.51575   21538187       67
## 66               Georgia    GA 32.15743  -82.90712   10711908      159
## 67               Georgia    GA 32.15743  -82.90712   10711908      159
## 68               Georgia    GA 32.15743  -82.90712   10711908      159
## 69               Georgia    GA 32.15743  -82.90712   10711908      159
## 70               Georgia    GA 32.15743  -82.90712   10711908      159
## 71               Georgia    GA 32.15743  -82.90712   10711908      159
## 72               Georgia    GA 32.15743  -82.90712   10711908      159
## 73               Georgia    GA 32.15743  -82.90712   10711908      159
## 74                Hawaii    HI 19.89868 -155.66586    1455271        5
## 75                Hawaii    HI 19.89868 -155.66586    1455271        5
## 76                Hawaii    HI 19.89868 -155.66586    1455271        5
## 77                Hawaii    HI 19.89868 -155.66586    1455271        5
## 78                Hawaii    HI 19.89868 -155.66586    1455271        5
## 79                  Iowa    IA 41.87800  -93.09770    3190369       99
## 80                  Iowa    IA 41.87800  -93.09770    3190369       99
## 81                  Iowa    IA 41.87800  -93.09770    3190369       99
## 82                  Iowa    IA 41.87800  -93.09770    3190369       99
## 83                  Iowa    IA 41.87800  -93.09770    3190369       99
## 84                 Idaho    ID 44.06820 -114.74204    1839106       44
## 85                 Idaho    ID 44.06820 -114.74204    1839106       44
## 86                 Idaho    ID 44.06820 -114.74204    1839106       44
## 87                 Idaho    ID 44.06820 -114.74204    1839106       44
## 88              Illinois    IL 40.63312  -89.39853   12812508      102
## 89              Illinois    IL 40.63312  -89.39853   12812508      102
## 90              Illinois    IL 40.63312  -89.39853   12812508      102
## 91              Illinois    IL 40.63312  -89.39853   12812508      102
## 92               Indiana    IN 40.55122  -85.60236    6785528       92
## 93               Indiana    IN 40.55122  -85.60236    6785528       92
## 94               Indiana    IN 40.55122  -85.60236    6785528       92
## 95                Kansas    KS 39.01190  -98.48425    2937880      105
## 96                Kansas    KS 39.01190  -98.48425    2937880      105
## 97                Kansas    KS 39.01190  -98.48425    2937880      105
## 98              Kentucky    KY 37.83933  -84.27002    4505836      120
## 99              Kentucky    KY 37.83933  -84.27002    4505836      120
## 100             Kentucky    KY 37.83933  -84.27002    4505836      120
## 101             Kentucky    KY 37.83933  -84.27002    4505836      120
## 102             Kentucky    KY 37.83933  -84.27002    4505836      120
## 103            Louisiana    LA 31.24482  -92.14502    4657757       64
## 104            Louisiana    LA 31.24482  -92.14502    4657757       64
## 105            Louisiana    LA 31.24482  -92.14502    4657757       64
## 106            Louisiana    LA 31.24482  -92.14502    4657757       64
## 107            Louisiana    LA 31.24482  -92.14502    4657757       64
## 108            Louisiana    LA 31.24482  -92.14502    4657757       64
## 109            Louisiana    LA 31.24482  -92.14502    4657757       64
## 110            Louisiana    LA 31.24482  -92.14502    4657757       64
## 111            Louisiana    LA 31.24482  -92.14502    4657757       64
## 112            Louisiana    LA 31.24482  -92.14502    4657757       64
## 113            Louisiana    LA 31.24482  -92.14502    4657757       64
## 114            Louisiana    LA 31.24482  -92.14502    4657757       64
## 115            Louisiana    LA 31.24482  -92.14502    4657757       64
## 116            Louisiana    LA 31.24482  -92.14502    4657757       64
## 117            Louisiana    LA 31.24482  -92.14502    4657757       64
## 118            Louisiana    LA 31.24482  -92.14502    4657757       64
## 119        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 120        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 121        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 122        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 123        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 124        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 125                Maine    ME 45.25378  -69.44547    1362359       16
## 126                Maine    ME 45.25378  -69.44547    1362359       16
## 127             Michigan    MI 44.31484  -85.60236   10077331       83
## 128             Michigan    MI 44.31484  -85.60236   10077331       83
## 129             Michigan    MI 44.31484  -85.60236   10077331       83
## 130             Michigan    MI 44.31484  -85.60236   10077331       83
## 131             Michigan    MI 44.31484  -85.60236   10077331       83
## 132             Michigan    MI 44.31484  -85.60236   10077331       83
## 133            Minnesota    MN 46.72955  -94.68590    5706494       87
## 134            Minnesota    MN 46.72955  -94.68590    5706494       87
## 135            Minnesota    MN 46.72955  -94.68590    5706494       87
## 136            Minnesota    MN 46.72955  -94.68590    5706494       87
## 137            Minnesota    MN 46.72955  -94.68590    5706494       87
## 138            Minnesota    MN 46.72955  -94.68590    5706494       87
## 139            Minnesota    MN 46.72955  -94.68590    5706494       87
## 140            Minnesota    MN 46.72955  -94.68590    5706494       87
## 141            Minnesota    MN 46.72955  -94.68590    5706494       87
## 142            Minnesota    MN 46.72955  -94.68590    5706494       87
## 143             Missouri    MO 37.96425  -91.83183    6154913      115
## 144             Missouri    MO 37.96425  -91.83183    6154913      115
## 145             Missouri    MO 37.96425  -91.83183    6154913      115
## 146             Missouri    MO 37.96425  -91.83183    6154913      115
## 147             Missouri    MO 37.96425  -91.83183    6154913      115
## 148             Missouri    MO 37.96425  -91.83183    6154913      115
## 149             Missouri    MO 37.96425  -91.83183    6154913      115
## 150          Mississippi    MS 32.35467  -89.39853    2961279       82
## 151          Mississippi    MS 32.35467  -89.39853    2961279       82
## 152          Mississippi    MS 32.35467  -89.39853    2961279       82
## 153          Mississippi    MS 32.35467  -89.39853    2961279       82
## 154          Mississippi    MS 32.35467  -89.39853    2961279       82
## 155          Mississippi    MS 32.35467  -89.39853    2961279       82
## 156          Mississippi    MS 32.35467  -89.39853    2961279       82
## 157          Mississippi    MS 32.35467  -89.39853    2961279       82
## 158              Montana    MT 46.87968 -110.36257    1084225       56
## 159              Montana    MT 46.87968 -110.36257    1084225       56
## 160       North Carolina    NC 35.75957  -79.01930   10439388      100
## 161       North Carolina    NC 35.75957  -79.01930   10439388      100
## 162       North Carolina    NC 35.75957  -79.01930   10439388      100
## 163       North Carolina    NC 35.75957  -79.01930   10439388      100
## 164       North Carolina    NC 35.75957  -79.01930   10439388      100
## 165       North Carolina    NC 35.75957  -79.01930   10439388      100
## 166       North Carolina    NC 35.75957  -79.01930   10439388      100
## 167       North Carolina    NC 35.75957  -79.01930   10439388      100
## 168       North Carolina    NC 35.75957  -79.01930   10439388      100
## 169         North Dakota    ND 47.55149 -101.00201     779094       53
## 170         North Dakota    ND 47.55149 -101.00201     779094       53
## 171         North Dakota    ND 47.55149 -101.00201     779094       53
## 172         North Dakota    ND 47.55149 -101.00201     779094       53
## 173             Nebraska    NE 41.49254  -99.90181    1961504       93
## 174             Nebraska    NE 41.49254  -99.90181    1961504       93
## 175             Nebraska    NE 41.49254  -99.90181    1961504       93
## 176             Nebraska    NE 41.49254  -99.90181    1961504       93
## 177             Nebraska    NE 41.49254  -99.90181    1961504       93
## 178        New Hampshire    NH 43.19385  -71.57240    1377529       10
## 179        New Hampshire    NH 43.19385  -71.57240    1377529       10
## 180        New Hampshire    NH 43.19385  -71.57240    1377529       10
## 181        New Hampshire    NH 43.19385  -71.57240    1377529       10
## 182           New Jersey    NJ 40.05832  -74.40566    9288994       21
## 183           New Jersey    NJ 40.05832  -74.40566    9288994       21
## 184           New Jersey    NJ 40.05832  -74.40566    9288994       21
## 185           New Mexico    NM 34.97273 -105.03236    2117522       33
## 186           New Mexico    NM 34.97273 -105.03236    2117522       33
## 187           New Mexico    NM 34.97273 -105.03236    2117522       33
## 188               Nevada    NV 38.80261 -116.41939    3104614       17
## 189               Nevada    NV 38.80261 -116.41939    3104614       17
## 190               Nevada    NV 38.80261 -116.41939    3104614       17
## 191             New York    NY 43.29943  -74.21793   20201249       62
## 192             New York    NY 43.29943  -74.21793   20201249       62
## 193             New York    NY 43.29943  -74.21793   20201249       62
## 194             New York    NY 43.29943  -74.21793   20201249       62
## 195             New York    NY 43.29943  -74.21793   20201249       62
## 196             New York    NY 43.29943  -74.21793   20201249       62
## 197                 Ohio    OH 40.41729  -82.90712   11799448       88
## 198                 Ohio    OH 40.41729  -82.90712   11799448       88
## 199                 Ohio    OH 40.41729  -82.90712   11799448       88
## 200                 Ohio    OH 40.41729  -82.90712   11799448       88
## 201                 Ohio    OH 40.41729  -82.90712   11799448       88
## 202                 Ohio    OH 40.41729  -82.90712   11799448       88
## 203             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 204             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 205             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 206             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 207             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 208             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 209             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 210             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 211             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 212             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 213               Oregon    OR 43.80413 -120.55420    4237256       36
## 214               Oregon    OR 43.80413 -120.55420    4237256       36
## 215               Oregon    OR 43.80413 -120.55420    4237256       36
## 216               Oregon    OR 43.80413 -120.55420    4237256       36
## 217               Oregon    OR 43.80413 -120.55420    4237256       36
## 218               Oregon    OR 43.80413 -120.55420    4237256       36
## 219               Oregon    OR 43.80413 -120.55420    4237256       36
## 220               Oregon    OR 43.80413 -120.55420    4237256       36
## 221         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 222         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 223         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 224         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 225         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 226         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 227          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 228          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 229          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 230          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 231          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 232          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 233          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 234          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 235          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 236         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 237         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 238         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 239         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 240         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 241       South Carolina    SC 33.83608  -81.16372    5118425       46
## 242       South Carolina    SC 33.83608  -81.16372    5118425       46
## 243       South Carolina    SC 33.83608  -81.16372    5118425       46
## 244       South Carolina    SC 33.83608  -81.16372    5118425       46
## 245       South Carolina    SC 33.83608  -81.16372    5118425       46
## 246       South Carolina    SC 33.83608  -81.16372    5118425       46
## 247       South Carolina    SC 33.83608  -81.16372    5118425       46
## 248       South Carolina    SC 33.83608  -81.16372    5118425       46
## 249         South Dakota    SD 43.96952  -99.90181     886667       66
## 250         South Dakota    SD 43.96952  -99.90181     886667       66
## 251         South Dakota    SD 43.96952  -99.90181     886667       66
## 252         South Dakota    SD 43.96952  -99.90181     886667       66
## 253         South Dakota    SD 43.96952  -99.90181     886667       66
## 254         South Dakota    SD 43.96952  -99.90181     886667       66
## 255         South Dakota    SD 43.96952  -99.90181     886667       66
## 256         South Dakota    SD 43.96952  -99.90181     886667       66
## 257         South Dakota    SD 43.96952  -99.90181     886667       66
## 258         South Dakota    SD 43.96952  -99.90181     886667       66
## 259            Tennessee    TN 35.51749  -86.58045    6910840       95
## 260            Tennessee    TN 35.51749  -86.58045    6910840       95
## 261            Tennessee    TN 35.51749  -86.58045    6910840       95
## 262            Tennessee    TN 35.51749  -86.58045    6910840       95
## 263            Tennessee    TN 35.51749  -86.58045    6910840       95
## 264            Tennessee    TN 35.51749  -86.58045    6910840       95
## 265            Tennessee    TN 35.51749  -86.58045    6910840       95
## 266                Texas    TX 31.96860  -99.90181   29145505      254
## 267                Texas    TX 31.96860  -99.90181   29145505      254
## 268                Texas    TX 31.96860  -99.90181   29145505      254
## 269                Texas    TX 31.96860  -99.90181   29145505      254
## 270                Texas    TX 31.96860  -99.90181   29145505      254
## 271                Texas    TX 31.96860  -99.90181   29145505      254
## 272                Texas    TX 31.96860  -99.90181   29145505      254
## 273                Texas    TX 31.96860  -99.90181   29145505      254
## 274                Texas    TX 31.96860  -99.90181   29145505      254
## 275                Texas    TX 31.96860  -99.90181   29145505      254
## 276                Texas    TX 31.96860  -99.90181   29145505      254
## 277                Texas    TX 31.96860  -99.90181   29145505      254
## 278                Texas    TX 31.96860  -99.90181   29145505      254
## 279                Texas    TX 31.96860  -99.90181   29145505      254
## 280                Texas    TX 31.96860  -99.90181   29145505      254
## 281                Texas    TX 31.96860  -99.90181   29145505      254
## 282                 Utah    UT 39.32098 -111.09373    3271616       29
## 283             Virginia    VA 37.43157  -78.65689    8631393      135
## 284             Virginia    VA 37.43157  -78.65689    8631393      135
## 285             Virginia    VA 37.43157  -78.65689    8631393      135
## 286             Virginia    VA 37.43157  -78.65689    8631393      135
## 287             Virginia    VA 37.43157  -78.65689    8631393      135
## 288             Virginia    VA 37.43157  -78.65689    8631393      135
## 289              Vermont    VT 44.55880  -72.57784     643077       14
## 290              Vermont    VT 44.55880  -72.57784     643077       14
## 291              Vermont    VT 44.55880  -72.57784     643077       14
## 292              Vermont    VT 44.55880  -72.57784     643077       14
## 293           Washington    WA 47.75107 -120.74014    7705281       39
## 294           Washington    WA 47.75107 -120.74014    7705281       39
## 295           Washington    WA 47.75107 -120.74014    7705281       39
## 296           Washington    WA 47.75107 -120.74014    7705281       39
## 297           Washington    WA 47.75107 -120.74014    7705281       39
## 298           Washington    WA 47.75107 -120.74014    7705281       39
## 299           Washington    WA 47.75107 -120.74014    7705281       39
## 300           Washington    WA 47.75107 -120.74014    7705281       39
## 301           Washington    WA 47.75107 -120.74014    7705281       39
## 302           Washington    WA 47.75107 -120.74014    7705281       39
## 303           Washington    WA 47.75107 -120.74014    7705281       39
## 304            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 305            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 306            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 307            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 308            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 309            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 310            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 311            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 312        West Virginia    WV 38.59763  -80.45490    1793716       55
## 313        West Virginia    WV 38.59763  -80.45490    1793716       55
## 314        West Virginia    WV 38.59763  -80.45490    1793716       55
## 315        West Virginia    WV 38.59763  -80.45490    1793716       55
## 316        West Virginia    WV 38.59763  -80.45490    1793716       55
## 317        West Virginia    WV 38.59763  -80.45490    1793716       55
## 318        West Virginia    WV 38.59763  -80.45490    1793716       55
## 319        West Virginia    WV 38.59763  -80.45490    1793716       55
## 320        West Virginia    WV 38.59763  -80.45490    1793716       55
## 321        West Virginia    WV 38.59763  -80.45490    1793716       55
## 322              Wyoming    WY 43.07597 -107.29028     576851       23
## 323              Wyoming    WY 43.07597 -107.29028     576851       23
## 324               Alaska    AK 63.58875 -154.49306     733391       34
## 325               Alaska    AK 63.58875 -154.49306     733391       34
## 326               Alaska    AK 63.58875 -154.49306     733391       34
## 327               Alaska    AK 63.58875 -154.49306     733391       34
## 328              Alabama    AL 32.31823  -86.90230    5024279       67
## 329              Alabama    AL 32.31823  -86.90230    5024279       67
## 330              Alabama    AL 32.31823  -86.90230    5024279       67
## 331              Alabama    AL 32.31823  -86.90230    5024279       67
## 332             Arkansas    AR 35.20105  -91.83183    3011524       75
## 333             Arkansas    AR 35.20105  -91.83183    3011524       75
## 334             Arkansas    AR 35.20105  -91.83183    3011524       75
## 335             Arkansas    AR 35.20105  -91.83183    3011524       75
## 336             Arkansas    AR 35.20105  -91.83183    3011524       75
## 337             Arkansas    AR 35.20105  -91.83183    3011524       75
## 338             Arkansas    AR 35.20105  -91.83183    3011524       75
## 339             Arkansas    AR 35.20105  -91.83183    3011524       75
## 340              Arizona    AZ 34.04893 -111.09373    7151502       15
## 341              Arizona    AZ 34.04893 -111.09373    7151502       15
## 342              Arizona    AZ 34.04893 -111.09373    7151502       15
## 343           California    CA 36.77826 -119.41793   39538223       58
## 344           California    CA 36.77826 -119.41793   39538223       58
## 345           California    CA 36.77826 -119.41793   39538223       58
## 346           California    CA 36.77826 -119.41793   39538223       58
## 347           California    CA 36.77826 -119.41793   39538223       58
## 348           California    CA 36.77826 -119.41793   39538223       58
## 349           California    CA 36.77826 -119.41793   39538223       58
## 350           California    CA 36.77826 -119.41793   39538223       58
## 351           California    CA 36.77826 -119.41793   39538223       58
## 352           California    CA 36.77826 -119.41793   39538223       58
## 353           California    CA 36.77826 -119.41793   39538223       58
## 354           California    CA 36.77826 -119.41793   39538223       58
## 355           California    CA 36.77826 -119.41793   39538223       58
## 356           California    CA 36.77826 -119.41793   39538223       58
## 357           California    CA 36.77826 -119.41793   39538223       58
## 358           California    CA 36.77826 -119.41793   39538223       58
## 359           California    CA 36.77826 -119.41793   39538223       58
## 360           California    CA 36.77826 -119.41793   39538223       58
## 361           California    CA 36.77826 -119.41793   39538223       58
## 362             Colorado    CO 39.55005 -105.78207    5773714       64
## 363             Colorado    CO 39.55005 -105.78207    5773714       64
## 364             Colorado    CO 39.55005 -105.78207    5773714       64
## 365             Colorado    CO 39.55005 -105.78207    5773714       64
## 366             Colorado    CO 39.55005 -105.78207    5773714       64
## 367          Connecticut    CT 41.60322  -73.08775    3605944        8
## 368          Connecticut    CT 41.60322  -73.08775    3605944        8
## 369          Connecticut    CT 41.60322  -73.08775    3605944        8
## 370          Connecticut    CT 41.60322  -73.08775    3605944        8
## 371          Connecticut    CT 41.60322  -73.08775    3605944        8
## 372          Connecticut    CT 41.60322  -73.08775    3605944        8
## 373 District of Columbia    DC 38.90599  -77.03342     689545        1
## 374 District of Columbia    DC 38.90599  -77.03342     689545        1
## 375             Delaware    DE 38.91083  -75.52767     989948        3
## 376              Florida    FL 27.66483  -81.51575   21538187       67
## 377              Florida    FL 27.66483  -81.51575   21538187       67
## 378              Florida    FL 27.66483  -81.51575   21538187       67
## 379              Florida    FL 27.66483  -81.51575   21538187       67
## 380              Florida    FL 27.66483  -81.51575   21538187       67
## 381              Florida    FL 27.66483  -81.51575   21538187       67
## 382              Florida    FL 27.66483  -81.51575   21538187       67
## 383              Florida    FL 27.66483  -81.51575   21538187       67
## 384              Florida    FL 27.66483  -81.51575   21538187       67
## 385              Florida    FL 27.66483  -81.51575   21538187       67
## 386              Florida    FL 27.66483  -81.51575   21538187       67
## 387              Florida    FL 27.66483  -81.51575   21538187       67
## 388              Florida    FL 27.66483  -81.51575   21538187       67
## 389              Georgia    GA 32.15743  -82.90712   10711908      159
## 390              Georgia    GA 32.15743  -82.90712   10711908      159
## 391              Georgia    GA 32.15743  -82.90712   10711908      159
## 392              Georgia    GA 32.15743  -82.90712   10711908      159
## 393              Georgia    GA 32.15743  -82.90712   10711908      159
## 394              Georgia    GA 32.15743  -82.90712   10711908      159
## 395              Georgia    GA 32.15743  -82.90712   10711908      159
## 396              Georgia    GA 32.15743  -82.90712   10711908      159
## 397               Hawaii    HI 19.89868 -155.66586    1455271        5
## 398               Hawaii    HI 19.89868 -155.66586    1455271        5
## 399               Hawaii    HI 19.89868 -155.66586    1455271        5
## 400               Hawaii    HI 19.89868 -155.66586    1455271        5
## 401               Hawaii    HI 19.89868 -155.66586    1455271        5
## 402                 Iowa    IA 41.87800  -93.09770    3190369       99
## 403                 Iowa    IA 41.87800  -93.09770    3190369       99
## 404                 Iowa    IA 41.87800  -93.09770    3190369       99
## 405                 Iowa    IA 41.87800  -93.09770    3190369       99
## 406                 Iowa    IA 41.87800  -93.09770    3190369       99
## 407                Idaho    ID 44.06820 -114.74204    1839106       44
## 408                Idaho    ID 44.06820 -114.74204    1839106       44
## 409                Idaho    ID 44.06820 -114.74204    1839106       44
## 410                Idaho    ID 44.06820 -114.74204    1839106       44
## 411             Illinois    IL 40.63312  -89.39853   12812508      102
## 412             Illinois    IL 40.63312  -89.39853   12812508      102
## 413             Illinois    IL 40.63312  -89.39853   12812508      102
## 414             Illinois    IL 40.63312  -89.39853   12812508      102
## 415              Indiana    IN 40.55122  -85.60236    6785528       92
## 416              Indiana    IN 40.55122  -85.60236    6785528       92
## 417              Indiana    IN 40.55122  -85.60236    6785528       92
## 418               Kansas    KS 39.01190  -98.48425    2937880      105
## 419               Kansas    KS 39.01190  -98.48425    2937880      105
## 420               Kansas    KS 39.01190  -98.48425    2937880      105
## 421             Kentucky    KY 37.83933  -84.27002    4505836      120
## 422             Kentucky    KY 37.83933  -84.27002    4505836      120
## 423             Kentucky    KY 37.83933  -84.27002    4505836      120
## 424             Kentucky    KY 37.83933  -84.27002    4505836      120
## 425             Kentucky    KY 37.83933  -84.27002    4505836      120
## 426            Louisiana    LA 31.24482  -92.14502    4657757       64
## 427            Louisiana    LA 31.24482  -92.14502    4657757       64
## 428            Louisiana    LA 31.24482  -92.14502    4657757       64
## 429            Louisiana    LA 31.24482  -92.14502    4657757       64
## 430            Louisiana    LA 31.24482  -92.14502    4657757       64
## 431            Louisiana    LA 31.24482  -92.14502    4657757       64
## 432            Louisiana    LA 31.24482  -92.14502    4657757       64
## 433            Louisiana    LA 31.24482  -92.14502    4657757       64
## 434            Louisiana    LA 31.24482  -92.14502    4657757       64
## 435            Louisiana    LA 31.24482  -92.14502    4657757       64
## 436            Louisiana    LA 31.24482  -92.14502    4657757       64
## 437            Louisiana    LA 31.24482  -92.14502    4657757       64
## 438            Louisiana    LA 31.24482  -92.14502    4657757       64
## 439            Louisiana    LA 31.24482  -92.14502    4657757       64
## 440            Louisiana    LA 31.24482  -92.14502    4657757       64
## 441            Louisiana    LA 31.24482  -92.14502    4657757       64
## 442        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 443        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 444        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 445        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 446        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 447        Massachusetts    MA 42.40721  -71.38244    7029917       14
## 448                Maine    ME 45.25378  -69.44547    1362359       16
## 449                Maine    ME 45.25378  -69.44547    1362359       16
## 450             Michigan    MI 44.31484  -85.60236   10077331       83
## 451             Michigan    MI 44.31484  -85.60236   10077331       83
## 452             Michigan    MI 44.31484  -85.60236   10077331       83
## 453             Michigan    MI 44.31484  -85.60236   10077331       83
## 454             Michigan    MI 44.31484  -85.60236   10077331       83
## 455             Michigan    MI 44.31484  -85.60236   10077331       83
## 456            Minnesota    MN 46.72955  -94.68590    5706494       87
## 457            Minnesota    MN 46.72955  -94.68590    5706494       87
## 458            Minnesota    MN 46.72955  -94.68590    5706494       87
## 459            Minnesota    MN 46.72955  -94.68590    5706494       87
## 460            Minnesota    MN 46.72955  -94.68590    5706494       87
## 461            Minnesota    MN 46.72955  -94.68590    5706494       87
## 462            Minnesota    MN 46.72955  -94.68590    5706494       87
## 463            Minnesota    MN 46.72955  -94.68590    5706494       87
## 464            Minnesota    MN 46.72955  -94.68590    5706494       87
## 465            Minnesota    MN 46.72955  -94.68590    5706494       87
## 466             Missouri    MO 37.96425  -91.83183    6154913      115
## 467             Missouri    MO 37.96425  -91.83183    6154913      115
## 468             Missouri    MO 37.96425  -91.83183    6154913      115
## 469             Missouri    MO 37.96425  -91.83183    6154913      115
## 470             Missouri    MO 37.96425  -91.83183    6154913      115
## 471             Missouri    MO 37.96425  -91.83183    6154913      115
## 472             Missouri    MO 37.96425  -91.83183    6154913      115
## 473          Mississippi    MS 32.35467  -89.39853    2961279       82
## 474          Mississippi    MS 32.35467  -89.39853    2961279       82
## 475          Mississippi    MS 32.35467  -89.39853    2961279       82
## 476          Mississippi    MS 32.35467  -89.39853    2961279       82
## 477          Mississippi    MS 32.35467  -89.39853    2961279       82
## 478          Mississippi    MS 32.35467  -89.39853    2961279       82
## 479          Mississippi    MS 32.35467  -89.39853    2961279       82
## 480          Mississippi    MS 32.35467  -89.39853    2961279       82
## 481              Montana    MT 46.87968 -110.36257    1084225       56
## 482              Montana    MT 46.87968 -110.36257    1084225       56
## 483       North Carolina    NC 35.75957  -79.01930   10439388      100
## 484       North Carolina    NC 35.75957  -79.01930   10439388      100
## 485       North Carolina    NC 35.75957  -79.01930   10439388      100
## 486       North Carolina    NC 35.75957  -79.01930   10439388      100
## 487       North Carolina    NC 35.75957  -79.01930   10439388      100
## 488       North Carolina    NC 35.75957  -79.01930   10439388      100
## 489       North Carolina    NC 35.75957  -79.01930   10439388      100
## 490       North Carolina    NC 35.75957  -79.01930   10439388      100
## 491       North Carolina    NC 35.75957  -79.01930   10439388      100
## 492         North Dakota    ND 47.55149 -101.00201     779094       53
## 493         North Dakota    ND 47.55149 -101.00201     779094       53
## 494         North Dakota    ND 47.55149 -101.00201     779094       53
## 495         North Dakota    ND 47.55149 -101.00201     779094       53
## 496             Nebraska    NE 41.49254  -99.90181    1961504       93
## 497             Nebraska    NE 41.49254  -99.90181    1961504       93
## 498             Nebraska    NE 41.49254  -99.90181    1961504       93
## 499             Nebraska    NE 41.49254  -99.90181    1961504       93
## 500             Nebraska    NE 41.49254  -99.90181    1961504       93
## 501        New Hampshire    NH 43.19385  -71.57240    1377529       10
## 502        New Hampshire    NH 43.19385  -71.57240    1377529       10
## 503        New Hampshire    NH 43.19385  -71.57240    1377529       10
## 504        New Hampshire    NH 43.19385  -71.57240    1377529       10
## 505           New Jersey    NJ 40.05832  -74.40566    9288994       21
## 506           New Jersey    NJ 40.05832  -74.40566    9288994       21
## 507           New Jersey    NJ 40.05832  -74.40566    9288994       21
## 508           New Mexico    NM 34.97273 -105.03236    2117522       33
## 509           New Mexico    NM 34.97273 -105.03236    2117522       33
## 510           New Mexico    NM 34.97273 -105.03236    2117522       33
## 511               Nevada    NV 38.80261 -116.41939    3104614       17
## 512               Nevada    NV 38.80261 -116.41939    3104614       17
## 513               Nevada    NV 38.80261 -116.41939    3104614       17
## 514             New York    NY 43.29943  -74.21793   20201249       62
## 515             New York    NY 43.29943  -74.21793   20201249       62
## 516             New York    NY 43.29943  -74.21793   20201249       62
## 517             New York    NY 43.29943  -74.21793   20201249       62
## 518             New York    NY 43.29943  -74.21793   20201249       62
## 519             New York    NY 43.29943  -74.21793   20201249       62
## 520                 Ohio    OH 40.41729  -82.90712   11799448       88
## 521                 Ohio    OH 40.41729  -82.90712   11799448       88
## 522                 Ohio    OH 40.41729  -82.90712   11799448       88
## 523                 Ohio    OH 40.41729  -82.90712   11799448       88
## 524                 Ohio    OH 40.41729  -82.90712   11799448       88
## 525                 Ohio    OH 40.41729  -82.90712   11799448       88
## 526             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 527             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 528             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 529             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 530             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 531             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 532             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 533             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 534             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 535             Oklahoma    OK 35.00775  -97.09288    3959353       77
## 536               Oregon    OR 43.80413 -120.55420    4237256       36
## 537               Oregon    OR 43.80413 -120.55420    4237256       36
## 538               Oregon    OR 43.80413 -120.55420    4237256       36
## 539               Oregon    OR 43.80413 -120.55420    4237256       36
## 540               Oregon    OR 43.80413 -120.55420    4237256       36
## 541               Oregon    OR 43.80413 -120.55420    4237256       36
## 542               Oregon    OR 43.80413 -120.55420    4237256       36
## 543               Oregon    OR 43.80413 -120.55420    4237256       36
## 544         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 545         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 546         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 547         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 548         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 549         Pennsylvania    PA 41.20332  -77.19452   13002700       67
## 550          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 551          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 552          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 553          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 554          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 555          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 556          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 557          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 558          Puerto Rico    PR 18.22083  -66.59015    3285874       78
## 559         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 560         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 561         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 562         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 563         Rhode Island    RI 41.58010  -71.47743    1097379        5
## 564       South Carolina    SC 33.83608  -81.16372    5118425       46
## 565       South Carolina    SC 33.83608  -81.16372    5118425       46
## 566       South Carolina    SC 33.83608  -81.16372    5118425       46
## 567       South Carolina    SC 33.83608  -81.16372    5118425       46
## 568       South Carolina    SC 33.83608  -81.16372    5118425       46
## 569       South Carolina    SC 33.83608  -81.16372    5118425       46
## 570       South Carolina    SC 33.83608  -81.16372    5118425       46
## 571       South Carolina    SC 33.83608  -81.16372    5118425       46
## 572         South Dakota    SD 43.96952  -99.90181     886667       66
## 573         South Dakota    SD 43.96952  -99.90181     886667       66
## 574         South Dakota    SD 43.96952  -99.90181     886667       66
## 575         South Dakota    SD 43.96952  -99.90181     886667       66
## 576         South Dakota    SD 43.96952  -99.90181     886667       66
## 577         South Dakota    SD 43.96952  -99.90181     886667       66
## 578         South Dakota    SD 43.96952  -99.90181     886667       66
## 579         South Dakota    SD 43.96952  -99.90181     886667       66
## 580         South Dakota    SD 43.96952  -99.90181     886667       66
## 581         South Dakota    SD 43.96952  -99.90181     886667       66
## 582            Tennessee    TN 35.51749  -86.58045    6910840       95
## 583            Tennessee    TN 35.51749  -86.58045    6910840       95
## 584            Tennessee    TN 35.51749  -86.58045    6910840       95
## 585            Tennessee    TN 35.51749  -86.58045    6910840       95
## 586            Tennessee    TN 35.51749  -86.58045    6910840       95
## 587            Tennessee    TN 35.51749  -86.58045    6910840       95
## 588            Tennessee    TN 35.51749  -86.58045    6910840       95
## 589                Texas    TX 31.96860  -99.90181   29145505      254
## 590                Texas    TX 31.96860  -99.90181   29145505      254
## 591                Texas    TX 31.96860  -99.90181   29145505      254
## 592                Texas    TX 31.96860  -99.90181   29145505      254
## 593                Texas    TX 31.96860  -99.90181   29145505      254
## 594                Texas    TX 31.96860  -99.90181   29145505      254
## 595                Texas    TX 31.96860  -99.90181   29145505      254
## 596                Texas    TX 31.96860  -99.90181   29145505      254
## 597                Texas    TX 31.96860  -99.90181   29145505      254
## 598                Texas    TX 31.96860  -99.90181   29145505      254
## 599                Texas    TX 31.96860  -99.90181   29145505      254
## 600                Texas    TX 31.96860  -99.90181   29145505      254
## 601                Texas    TX 31.96860  -99.90181   29145505      254
## 602                Texas    TX 31.96860  -99.90181   29145505      254
## 603                Texas    TX 31.96860  -99.90181   29145505      254
## 604                Texas    TX 31.96860  -99.90181   29145505      254
## 605                 Utah    UT 39.32098 -111.09373    3271616       29
## 606             Virginia    VA 37.43157  -78.65689    8631393      135
## 607             Virginia    VA 37.43157  -78.65689    8631393      135
## 608             Virginia    VA 37.43157  -78.65689    8631393      135
## 609             Virginia    VA 37.43157  -78.65689    8631393      135
## 610             Virginia    VA 37.43157  -78.65689    8631393      135
## 611             Virginia    VA 37.43157  -78.65689    8631393      135
## 612              Vermont    VT 44.55880  -72.57784     643077       14
## 613              Vermont    VT 44.55880  -72.57784     643077       14
## 614              Vermont    VT 44.55880  -72.57784     643077       14
## 615              Vermont    VT 44.55880  -72.57784     643077       14
## 616           Washington    WA 47.75107 -120.74014    7705281       39
## 617           Washington    WA 47.75107 -120.74014    7705281       39
## 618           Washington    WA 47.75107 -120.74014    7705281       39
## 619           Washington    WA 47.75107 -120.74014    7705281       39
## 620           Washington    WA 47.75107 -120.74014    7705281       39
## 621           Washington    WA 47.75107 -120.74014    7705281       39
## 622           Washington    WA 47.75107 -120.74014    7705281       39
## 623           Washington    WA 47.75107 -120.74014    7705281       39
## 624           Washington    WA 47.75107 -120.74014    7705281       39
## 625           Washington    WA 47.75107 -120.74014    7705281       39
## 626           Washington    WA 47.75107 -120.74014    7705281       39
## 627            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 628            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 629            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 630            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 631            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 632            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 633            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 634            Wisconsin    WI 43.78444  -88.78787    5893718       72
## 635        West Virginia    WV 38.59763  -80.45490    1793716       55
## 636        West Virginia    WV 38.59763  -80.45490    1793716       55
## 637        West Virginia    WV 38.59763  -80.45490    1793716       55
## 638        West Virginia    WV 38.59763  -80.45490    1793716       55
## 639        West Virginia    WV 38.59763  -80.45490    1793716       55
## 640        West Virginia    WV 38.59763  -80.45490    1793716       55
## 641        West Virginia    WV 38.59763  -80.45490    1793716       55
## 642        West Virginia    WV 38.59763  -80.45490    1793716       55
## 643        West Virginia    WV 38.59763  -80.45490    1793716       55
## 644        West Virginia    WV 38.59763  -80.45490    1793716       55
## 645              Wyoming    WY 43.07597 -107.29028     576851       23
## 646              Wyoming    WY 43.07597 -107.29028     576851       23
##      FEMARegion disasterNumber declarationType
## 1      Region X           4122              DR
## 2      Region X           4533              DR
## 3      Region X           4413              DR
## 4      Region X           4094              DR
## 5     Region IV           4503              DR
## 6     Region IV           4362              DR
## 7     Region IV           4563              DR
## 8     Region IV           4176              DR
## 9     Region VI           4226              DR
## 10    Region VI           4174              DR
## 11    Region VI           4318              DR
## 12    Region VI           4254              DR
## 13    Region VI           4160              DR
## 14    Region VI           4518              DR
## 15    Region VI           4441              DR
## 16    Region VI           4270              DR
## 17    Region IX           3442              EM
## 18    Region IX           4524              DR
## 19    Region IX           4582              DR
## 20    Region IX           4482              DR
## 21    Region IX           3396              EM
## 22    Region IX           3381              EM
## 23    Region IX           4431              DR
## 24    Region IX           3428              EM
## 25    Region IX           4569              DR
## 26    Region IX           3415              EM
## 27    Region IX           4382              DR
## 28    Region IX           4353              DR
## 29    Region IX           4558              DR
## 30    Region IX           4407              DR
## 31    Region IX           4619              DR
## 32    Region IX           3398              EM
## 33    Region IX           4301              DR
## 34    Region IX           3409              EM
## 35    Region IX           4240              DR
## 36    Region IX           4344              DR
## 37    Region IX           4610              DR
## 38    Region IX           4308              DR
## 39  Region VIII           4229              DR
## 40  Region VIII           4067              DR
## 41  Region VIII           4498              DR
## 42  Region VIII           3365              EM
## 43  Region VIII           4145              DR
## 44     Region I           3535              EM
## 45     Region I           4500              DR
## 46     Region I           4087              DR
## 47     Region I           4580              DR
## 48     Region I           4106              DR
## 49     Region I           3353              EM
## 50   Region III           4502              DR
## 51   Region III           3553              EM
## 52   Region III           4526              DR
## 53    Region IV           3419              EM
## 54    Region IV           3560              EM
## 55    Region IV           3377              EM
## 56    Region IV           3405              EM
## 57    Region IV           4177              DR
## 58    Region IV           4283              DR
## 59    Region IV           4564              DR
## 60    Region IV           4068              DR
## 61    Region IV           4337              DR
## 62    Region IV           4341              DR
## 63    Region IV           4486              DR
## 64    Region IV           4399              DR
## 65    Region IV           3385              EM
## 66    Region IV           3422              EM
## 67    Region IV           4294              DR
## 68    Region IV           4501              DR
## 69    Region IV           4284              DR
## 70    Region IV           4297              DR
## 71    Region IV           4338              DR
## 72    Region IV           4400              DR
## 73    Region IV           3379              EM
## 74    Region IX           3399              EM
## 75    Region IX           4510              DR
## 76    Region IX           4366              DR
## 77    Region IX           4365              DR
## 78    Region IX           3529              EM
## 79   Region VII           4557              DR
## 80   Region VII           4126              DR
## 81   Region VII           4119              DR
## 82   Region VII           4421              DR
## 83   Region VII           4483              DR
## 84     Region X           4313              DR
## 85     Region X           4246              DR
## 86     Region X           4534              DR
## 87     Region X           4310              DR
## 88     Region V           4489              DR
## 89     Region V           4157              DR
## 90     Region V           4461              DR
## 91     Region V           4116              DR
## 92     Region V           4058              DR
## 93     Region V           4363              DR
## 94     Region V           4515              DR
## 95   Region VII           4504              DR
## 96   Region VII           3412              EM
## 97   Region VII           4063              DR
## 98    Region IV           4218              DR
## 99    Region IV           4497              DR
## 100   Region IV           4057              DR
## 101   Region IV           4358              DR
## 102   Region IV           4428              DR
## 103   Region VI           4570              DR
## 104   Region VI           3549              EM
## 105   Region VI           3416              EM
## 106   Region VI           4277              DR
## 107   Region VI           3538              EM
## 108   Region VI           4080              DR
## 109   Region VI           4611              DR
## 110   Region VI           3392              EM
## 111   Region VI           4559              DR
## 112   Region VI           3347              EM
## 113   Region VI           4577              DR
## 114   Region VI           4263              DR
## 115   Region VI           3543              EM
## 116   Region VI           3382              EM
## 117   Region VI           4484              DR
## 118   Region VI           3547              EM
## 119    Region I           3362              EM
## 120    Region I           4372              DR
## 121    Region I           3350              EM
## 122    Region I           4214              DR
## 123    Region I           4496              DR
## 124    Region I           4379              DR
## 125    Region I           4522              DR
## 126    Region I           4354              DR
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## 613    Region I           3437              EM
## 614    Region I           4532              DR
## 615    Region I           4445              DR
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## 642  Region III           4071              DR
## 643  Region III           4059              DR
## 644  Region III           4210              DR
## 645 Region VIII           4535              DR
## 646 Region VIII           4227              DR
##                                                                      declarationTitle
## 1                                                                            FLOODING
## 2                                                                   COVID-19 PANDEMIC
## 3                                                                          EARTHQUAKE
## 4                         SEVERE STORM, STRAIGHT-LINE WINDS, FLOODING, AND LANDSLIDES
## 5                                                                   COVID-19 PANDEMIC
## 6                                                         SEVERE STORMS AND TORNADOES
## 7                                                                     HURRICANE SALLY
## 8                         SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 9                         SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 10                                              SEVERE STORMS,TORNADOES, AND FLOODING
## 11                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 12                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 13                                                                SEVERE WINTER STORM
## 14                                                                  COVID-19 PANDEMIC
## 15                                                         SEVERE STORMS AND FLOODING
## 16                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 17                                                                           COVID-19
## 18                                                                  COVID-19 PANDEMIC
## 19                                                                  COVID-19 PANDEMIC
## 20                                                                  COVID-19 PANDEMIC
## 21                                                                          WILDFIRES
## 22                       POTENTIAL FAILURE OF THE EMERGENCY SPILLWAY AT OROVILLE LAKE
## 23                          SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 24                                                                           COVID-19
## 25                                                                          WILDFIRES
## 26                                                                        EARTHQUAKES
## 27                                                           WILDFIRES AND HIGH WINDS
## 28                                    WILDFIRES, FLOODING, MUDFLOWS, AND DEBRIS FLOWS
## 29                                                                          WILDFIRES
## 30                                                                          WILDFIRES
## 31                                                                        CALDOR FIRE
## 32                                                                           WILDFIRE
## 33                                      SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 34                                                                          WILDFIRES
## 35                                                         VALLEY FIRE AND BUTTE FIRE
## 36                                                                          WILDFIRES
## 37                                                                          WILDFIRES
## 38                                      SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 39                      SEVERE STORMS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## 40                                               HIGH PARK AND WALDO CANYON WILDFIRES
## 41                                                                  COVID-19 PANDEMIC
## 42                                 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 43                                 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 44                                                              TROPICAL STORM ISAIAS
## 45                                                                  COVID-19 PANDEMIC
## 46                                                                    HURRICANE SANDY
## 47                                                              TROPICAL STORM ISAIAS
## 48                                                  SEVERE WINTER STORM AND SNOWSTORM
## 49                                                                    HURRICANE SANDY
## 50                                                                  COVID-19 PANDEMIC
## 51                                                     59TH PRESIDENTIAL INAUGURATION
## 52                                                                  COVID-19 PANDEMIC
## 53                                                                   HURRICANE DORIAN
## 54                                                         SURFSIDE BUILDING COLLAPSE
## 55                                                                  HURRICANE MATTHEW
## 56                                                                  HURRICANE MICHAEL
## 57                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 58                                                                  HURRICANE MATTHEW
## 59                                                                    HURRICANE SALLY
## 60                                                               TROPICAL STORM DEBBY
## 61                                                                     HURRICANE IRMA
## 62                                         HURRICANE IRMA - SEMINOLE TRIBE OF FLORIDA
## 63                                                                  COVID-19 PANDEMIC
## 64                                                                  HURRICANE MICHAEL
## 65                                                                     HURRICANE IRMA
## 66                                                                   HURRICANE DORIAN
## 67                                  SEVERE STORMS, TORNADOES, AND STRAIGHT-LINE WINDS
## 68                                                                  COVID-19 PANDEMIC
## 69                                                                  HURRICANE MATTHEW
## 70                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 71                                                                     HURRICANE IRMA
## 72                                                                  HURRICANE MICHAEL
## 73                                                                  HURRICANE MATTHEW
## 74                                                                     HURRICANE LANE
## 75                                                                  COVID-19 PANDEMIC
## 76                                          KILAUEA VOLCANIC ERUPTION AND EARTHQUAKES
## 77                                 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 78                                                                  HURRICANE DOUGLAS
## 79                                                                      SEVERE STORMS
## 80                                             SEVERE STORMS, TORNADOES, AND FLOODING
## 81                                   SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 82                                                         SEVERE STORMS AND FLOODING
## 83                                                                  COVID-19 PANDEMIC
## 84                                 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 85                                                SEVERE STORM AND STRAIGHT-LINE WIND
## 86                                                                  COVID-19 PANDEMIC
## 87                                                  SEVERE WINTER STORMS AND FLOODING
## 88                                                                  COVID-19 PANDEMIC
## 89                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 90                                                         SEVERE STORMS AND FLOODING
## 91                                   SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 92                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 93                                                         SEVERE STORMS AND FLOODING
## 94                                                                  COVID-19 PANDEMIC
## 95                                                                  COVID-19 PANDEMIC
## 96                                                             TORNADOES AND FLOODING
## 97                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 98                SEVERE WINTER STORM, SNOWSTORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 99                                                                  COVID-19 PANDEMIC
## 100                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 101                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 102           SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 103                                                                   HURRICANE DELTA
## 104                                                               TROPICAL STORM ZETA
## 105                                                              TROPICAL STORM BARRY
## 106                                                        SEVERE STORMS AND FLOODING
## 107                                                  TROPICAL STORMS LAURA  AND MARCO
## 108                                                                   HURRICANE ISAAC
## 109                                                                     HURRICANE IDA
## 110                                                               TROPICAL STORM NATE
## 111                                                                   HURRICANE LAURA
## 112                                                              TROPICAL STORM ISAAC
## 113                                                                    HURRICANE ZETA
## 114                                                        SEVERE STORMS AND FLOODING
## 115                                                                   HURRICANE SALLY
## 116                                                             TROPICAL STORM HARVEY
## 117                                                                 COVID-19 PANDEMIC
## 118                                                                   HURRICANE DELTA
## 119                                                                        EXPLOSIONS
## 120                                                  SEVERE WINTER STORM AND FLOODING
## 121                                                                   HURRICANE SANDY
## 122                                      SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 123                                                                 COVID-19 PANDEMIC
## 124                                                 SEVERE WINTER STORM AND SNOWSTORM
## 125                                                                 COVID-19 PANDEMIC
## 126                                                         SEVERE STORM AND FLOODING
## 127                                                        SEVERE STORMS AND FLOODING
## 128                                                                 COVID-19 PANDEMIC
## 129                                            SEVERE STORMS, FLOODING, AND TORNADOES
## 130                                                                          FLOODING
## 131                                                        SEVERE STORMS AND FLOODING
## 132                                                        SEVERE STORMS AND FLOODING
## 133                                                                          COVID-19
## 134                            SEVERE WINTER STORM, STRAIGHT-LINE WINDS, AND FLOODING
## 135                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 136                                                        SEVERE STORMS AND FLOODING
## 137                                                               SEVERE WINTER STORM
## 138                                                        SEVERE STORMS AND FLOODING
## 139                                                        SEVERE STORMS AND FLOODING
## 140                                                                 COVID-19 PANDEMIC
## 141                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 142           SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 143                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 144                                                                 COVID-19 PANDEMIC
## 145                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 146                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 147                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 148                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 149                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 150                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 151                                                                   HURRICANE ISAAC
## 152                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 153                                                                     HURRICANE IDA
## 154                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 155                                                        SEVERE STORMS AND FLOODING
## 156                                                                 COVID-19 PANDEMIC
## 157                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 158                                                                          FLOODING
## 159                                                                 COVID-19 PANDEMIC
## 160                                                                TROPICAL STORM ETA
## 161                                                                 HURRICANE MATTHEW
## 162                                                                  HURRICANE DORIAN
## 163                                                                  HURRICANE DORIAN
## 164                                                                HURRICANE FLORENCE
## 165                                                                 COVID-19 PANDEMIC
## 166                                                                 HURRICANE MATTHEW
## 167                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 168                                                            TROPICAL STORM MICHAEL
## 169                                                        SEVERE STORMS AND FLOODING
## 170                                                                          FLOODING
## 171                                                                          FLOODING
## 172                                                                 COVID-19 PANDEMIC
## 173                                                        SEVERE STORMS AND FLOODING
## 174                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 175                                                                 COVID-19 PANDEMIC
## 176                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 177                            SEVERE WINTER STORM, STRAIGHT-LINE WINDS, AND FLOODING
## 178                                                         SEVERE STORM AND FLOODING
## 179                                                 SEVERE WINTER STORM AND SNOWSTORM
## 180                                                         SEVERE STORM AND FLOODING
## 181                                                                 COVID-19 PANDEMIC
## 182                                                                   HURRICANE SANDY
## 183                                                                 COVID-19 PANDEMIC
## 184                                                         REMNANTS OF HURRICANE IDA
## 185                                                        SEVERE STORMS AND FLOODING
## 186                                                        SEVERE STORMS AND FLOODING
## 187                                                                 COVID-19 PANDEMIC
## 188                                                                 COVID-19 PANDEMIC
## 189                                     SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 190                                     SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 191                                                                   HURRICANE HENRI
## 192                                                                 COVID-19 PANDEMIC
## 193                                                                   HURRICANE SANDY
## 194                                                        SEVERE STORMS AND FLOODING
## 195                                                         REMNANTS OF HURRICANE IDA
## 196                                                        SEVERE STORMS AND FLOODING
## 197                                                                 COVID-19 PANDEMIC
## 198                                                                     SEVERE STORMS
## 199                                             SEVERE STORMS AND STRAIGHT-LINE WINDS
## 200 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDE
## 201                                          SEVERE STORMS, LANDSLIDES, AND MUDSLIDES
## 202                 SEVERE STORMS AND FLOODING DUE TO THE REMNANTS OF HURRICANE SANDY
## 203                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 204                                                                          COVID-19
## 205                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 206                                                                          COVID-19
## 207                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 208                                                                          COVID-19
## 209                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 210                                                                 COVID-19 PANDEMIC
## 211                                                       FREEDOM AND NOBLE WILDFIRES
## 212                       SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 213                         SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 214                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 215                                                  SEVERE WINTER STORM AND FLOODING
## 216                         SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 217                                                 WILDFIRES AND STRAIGHT-LINE WINDS
## 218                          SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 219                                                                 COVID-19 PANDEMIC
## 220    SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 221                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 222                                                                   HURRICANE SANDY
## 223                                                         REMNANTS OF HURRICANE IDA
## 224                                                                 COVID-19 PANDEMIC
## 225                                                        SEVERE STORMS AND FLOODING
## 226                                                               SEVERE WINTER STORM
## 227                                                                       EARTHQUAKES
## 228                                                         SEVERE STORM AND FLOODING
## 229                                                                       EARTHQUAKES
## 230                                                   POTENTIAL TROPICAL CYCLONE NINE
## 231                                                                 COVID-19 PANDEMIC
## 232                                                             TROPICAL STORM DORIAN
## 233                                                                   HURRICANE MARIA
## 234                                                                    HURRICANE IRMA
## 235                                                                   HURRICANE MARIA
## 236                                                                   HURRICANE HENRI
## 237                                                 SEVERE WINTER STORM AND SNOWSTORM
## 238                                                                   HURRICANE SANDY
## 239                                                                   HURRICANE SANDY
## 240                                                                 COVID-19 PANDEMIC
## 241                                                                    HURRICANE IRMA
## 242                                                        SEVERE STORMS AND FLOODING
## 243                                                                HURRICANE FLORENCE
## 244                                                        SEVERE STORMS AND FLOODING
## 245                                                                  HURRICANE DORIAN
## 246                                                                 HURRICANE MATTHEW
## 247                                                                 HURRICANE MATTHEW
## 248                                                                 COVID-19 PANDEMIC
## 249                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 250                                                                 COVID-19 PANDEMIC
## 251                                      SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 252                                              SEVERE STORMS, TORNADO, AND FLOODING
## 253                                                 SEVERE WINTER STORM AND SNOWSTORM
## 254                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 255                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 256                                                               SEVERE WINTER STORM
## 257                                                                          COVID-19
## 258                                      SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 259                                                                         WILDFIRES
## 260                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 261                                                                 COVID-19 PANDEMIC
## 262                                                  SEVERE WINTER STORM AND FLOODING
## 263                                 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND
## 264                                                         SEVERE STORM AND FLOODING
## 265                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 266                                                              SEVERE WINTER STORMS
## 267                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 268                                                        SEVERE STORMS AND FLOODING
## 269                                                        SEVERE STORMS AND FLOODING
## 270                                                        SEVERE STORMS AND FLOODING
## 271                                                        SEVERE STORMS AND FLOODING
## 272                                                                   HURRICANE LAURA
## 273                                                                   HURRICANE HANNA
## 274                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 275                                                                  HURRICANE HARVEY
## 276                                                                         EXPLOSION
## 277                                                        SEVERE STORMS AND FLOODING
## 278                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 279                SEVERE WINTER STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 280                                                                 COVID-19 PANDEMIC
## 281                                                                         EXPLOSION
## 282                                                                 COVID-19 PANDEMIC
## 283                                                                 HURRICANE MATTHEW
## 284                                                                HURRICANE FLORENCE
## 285                                                                   HURRICANE SANDY
## 286                                                                 COVID-19 PANDEMIC
## 287                                             SEVERE STORMS AND STRAIGHT-LINE WINDS
## 288                                                                HURRICANE FLORENCE
## 289                                                         SEVERE STORM AND FLOODING
## 290                                                                          COVID-19
## 291                                                                 COVID-19 PANDEMIC
## 292                                                        SEVERE STORMS AND FLOODING
## 293     SEVERE WINTER STORM, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 294                            SEVERE WINTER STORMS, FLOODING, LANDSLIDES,  MUDSLIDES
## 295                                                                 COVID-19 PANDEMIC
## 296           SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 297                                                           WILDFIRES AND MUDSLIDES
## 298                          SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 299                                                                         WILDFIRES
## 300                                                                         WILDFIRES
## 301                                                            FLOODING AND MUDSLIDES
## 302                                                                         WILDFIRES
## 303                                                            FLOODING AND MUDSLIDES
## 304                                                        SEVERE STORMS AND FLOODING
## 305           SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, FLOODING, AND LANDSLIDES
## 306                                            SEVERE STORMS, FLOODING, AND MUDSLIDES
## 307                                                                 COVID-19 PANDEMIC
## 308                                            SEVERE STORMS, FLOODING, AND MUDSLIDES
## 309                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 310                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 311                                                        SEVERE STORMS AND FLOODING
## 312                                                                   HURRICANE SANDY
## 313                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 314                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 315                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 316                                                                   HURRICANE SANDY
## 317                                 SEVERE STORMS, FLOODING, MUDSLIDES, AND LANSLIDES
## 318                                                                 COVID-19 PANDEMIC
## 319                                             SEVERE STORMS AND STRAIGHT-LINE WINDS
## 320                     SEVERE STORMS, TORNADOES, FLOODING, MUDSLIDES, AND LANDSLIDES
## 321                          SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 322                                                                 COVID-19 PANDEMIC
## 323                                                        SEVERE STORMS AND FLOODING
## 324                                                                          FLOODING
## 325                                                                 COVID-19 PANDEMIC
## 326                                                                        EARTHQUAKE
## 327                       SEVERE STORM, STRAIGHT-LINE WINDS, FLOODING, AND LANDSLIDES
## 328                                                                 COVID-19 PANDEMIC
## 329                                                       SEVERE STORMS AND TORNADOES
## 330                                                                   HURRICANE SALLY
## 331                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 332                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 333                                             SEVERE STORMS,TORNADOES, AND FLOODING
## 334                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 335                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 336                                                               SEVERE WINTER STORM
## 337                                                                 COVID-19 PANDEMIC
## 338                                                        SEVERE STORMS AND FLOODING
## 339                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 340                                                                          COVID-19
## 341                                                                 COVID-19 PANDEMIC
## 342                                                                 COVID-19 PANDEMIC
## 343                                                                 COVID-19 PANDEMIC
## 344                                                                         WILDFIRES
## 345                      POTENTIAL FAILURE OF THE EMERGENCY SPILLWAY AT OROVILLE LAKE
## 346                         SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 347                                                                          COVID-19
## 348                                                                         WILDFIRES
## 349                                                                       EARTHQUAKES
## 350                                                          WILDFIRES AND HIGH WINDS
## 351                                   WILDFIRES, FLOODING, MUDFLOWS, AND DEBRIS FLOWS
## 352                                                                         WILDFIRES
## 353                                                                         WILDFIRES
## 354                                                                       CALDOR FIRE
## 355                                                                          WILDFIRE
## 356                                     SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 357                                                                         WILDFIRES
## 358                                                        VALLEY FIRE AND BUTTE FIRE
## 359                                                                         WILDFIRES
## 360                                                                         WILDFIRES
## 361                                     SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 362                     SEVERE STORMS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## 363                                              HIGH PARK AND WALDO CANYON WILDFIRES
## 364                                                                 COVID-19 PANDEMIC
## 365                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 366                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 367                                                             TROPICAL STORM ISAIAS
## 368                                                                 COVID-19 PANDEMIC
## 369                                                                   HURRICANE SANDY
## 370                                                             TROPICAL STORM ISAIAS
## 371                                                 SEVERE WINTER STORM AND SNOWSTORM
## 372                                                                   HURRICANE SANDY
## 373                                                                 COVID-19 PANDEMIC
## 374                                                    59TH PRESIDENTIAL INAUGURATION
## 375                                                                 COVID-19 PANDEMIC
## 376                                                                  HURRICANE DORIAN
## 377                                                        SURFSIDE BUILDING COLLAPSE
## 378                                                                 HURRICANE MATTHEW
## 379                                                                 HURRICANE MICHAEL
## 380                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 381                                                                 HURRICANE MATTHEW
## 382                                                                   HURRICANE SALLY
## 383                                                              TROPICAL STORM DEBBY
## 384                                                                    HURRICANE IRMA
## 385                                        HURRICANE IRMA - SEMINOLE TRIBE OF FLORIDA
## 386                                                                 COVID-19 PANDEMIC
## 387                                                                 HURRICANE MICHAEL
## 388                                                                    HURRICANE IRMA
## 389                                                                  HURRICANE DORIAN
## 390                                 SEVERE STORMS, TORNADOES, AND STRAIGHT-LINE WINDS
## 391                                                                 COVID-19 PANDEMIC
## 392                                                                 HURRICANE MATTHEW
## 393                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 394                                                                    HURRICANE IRMA
## 395                                                                 HURRICANE MICHAEL
## 396                                                                 HURRICANE MATTHEW
## 397                                                                    HURRICANE LANE
## 398                                                                 COVID-19 PANDEMIC
## 399                                         KILAUEA VOLCANIC ERUPTION AND EARTHQUAKES
## 400                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 401                                                                 HURRICANE DOUGLAS
## 402                                                                     SEVERE STORMS
## 403                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 404                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 405                                                        SEVERE STORMS AND FLOODING
## 406                                                                 COVID-19 PANDEMIC
## 407                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 408                                               SEVERE STORM AND STRAIGHT-LINE WIND
## 409                                                                 COVID-19 PANDEMIC
## 410                                                 SEVERE WINTER STORMS AND FLOODING
## 411                                                                 COVID-19 PANDEMIC
## 412                                 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 413                                                        SEVERE STORMS AND FLOODING
## 414                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 415                                 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 416                                                        SEVERE STORMS AND FLOODING
## 417                                                                 COVID-19 PANDEMIC
## 418                                                                 COVID-19 PANDEMIC
## 419                                                            TORNADOES AND FLOODING
## 420                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 421               SEVERE WINTER STORM, SNOWSTORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 422                                                                 COVID-19 PANDEMIC
## 423                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 424                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 425           SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 426                                                                   HURRICANE DELTA
## 427                                                               TROPICAL STORM ZETA
## 428                                                              TROPICAL STORM BARRY
## 429                                                        SEVERE STORMS AND FLOODING
## 430                                                  TROPICAL STORMS LAURA  AND MARCO
## 431                                                                   HURRICANE ISAAC
## 432                                                                     HURRICANE IDA
## 433                                                               TROPICAL STORM NATE
## 434                                                                   HURRICANE LAURA
## 435                                                              TROPICAL STORM ISAAC
## 436                                                                    HURRICANE ZETA
## 437                                                        SEVERE STORMS AND FLOODING
## 438                                                                   HURRICANE SALLY
## 439                                                             TROPICAL STORM HARVEY
## 440                                                                 COVID-19 PANDEMIC
## 441                                                                   HURRICANE DELTA
## 442                                                                        EXPLOSIONS
## 443                                                  SEVERE WINTER STORM AND FLOODING
## 444                                                                   HURRICANE SANDY
## 445                                      SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 446                                                                 COVID-19 PANDEMIC
## 447                                                 SEVERE WINTER STORM AND SNOWSTORM
## 448                                                                 COVID-19 PANDEMIC
## 449                                                         SEVERE STORM AND FLOODING
## 450                                                        SEVERE STORMS AND FLOODING
## 451                                                                 COVID-19 PANDEMIC
## 452                                            SEVERE STORMS, FLOODING, AND TORNADOES
## 453                                                                          FLOODING
## 454                                                        SEVERE STORMS AND FLOODING
## 455                                                        SEVERE STORMS AND FLOODING
## 456                                                                          COVID-19
## 457                            SEVERE WINTER STORM, STRAIGHT-LINE WINDS, AND FLOODING
## 458                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 459                                                        SEVERE STORMS AND FLOODING
## 460                                                               SEVERE WINTER STORM
## 461                                                        SEVERE STORMS AND FLOODING
## 462                                                        SEVERE STORMS AND FLOODING
## 463                                                                 COVID-19 PANDEMIC
## 464                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 465           SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 466                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 467                                                                 COVID-19 PANDEMIC
## 468                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 469                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 470                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 471                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 472                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 473                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 474                                                                   HURRICANE ISAAC
## 475                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 476                                                                     HURRICANE IDA
## 477                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 478                                                        SEVERE STORMS AND FLOODING
## 479                                                                 COVID-19 PANDEMIC
## 480                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 481                                                                          FLOODING
## 482                                                                 COVID-19 PANDEMIC
## 483                                                                TROPICAL STORM ETA
## 484                                                                 HURRICANE MATTHEW
## 485                                                                  HURRICANE DORIAN
## 486                                                                  HURRICANE DORIAN
## 487                                                                HURRICANE FLORENCE
## 488                                                                 COVID-19 PANDEMIC
## 489                                                                 HURRICANE MATTHEW
## 490                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 491                                                            TROPICAL STORM MICHAEL
## 492                                                        SEVERE STORMS AND FLOODING
## 493                                                                          FLOODING
## 494                                                                          FLOODING
## 495                                                                 COVID-19 PANDEMIC
## 496                                                        SEVERE STORMS AND FLOODING
## 497                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 498                                                                 COVID-19 PANDEMIC
## 499                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 500                            SEVERE WINTER STORM, STRAIGHT-LINE WINDS, AND FLOODING
## 501                                                         SEVERE STORM AND FLOODING
## 502                                                 SEVERE WINTER STORM AND SNOWSTORM
## 503                                                         SEVERE STORM AND FLOODING
## 504                                                                 COVID-19 PANDEMIC
## 505                                                                   HURRICANE SANDY
## 506                                                                 COVID-19 PANDEMIC
## 507                                                         REMNANTS OF HURRICANE IDA
## 508                                                        SEVERE STORMS AND FLOODING
## 509                                                        SEVERE STORMS AND FLOODING
## 510                                                                 COVID-19 PANDEMIC
## 511                                                                 COVID-19 PANDEMIC
## 512                                     SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 513                                     SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 514                                                                   HURRICANE HENRI
## 515                                                                 COVID-19 PANDEMIC
## 516                                                                   HURRICANE SANDY
## 517                                                        SEVERE STORMS AND FLOODING
## 518                                                         REMNANTS OF HURRICANE IDA
## 519                                                        SEVERE STORMS AND FLOODING
## 520                                                                 COVID-19 PANDEMIC
## 521                                                                     SEVERE STORMS
## 522                                             SEVERE STORMS AND STRAIGHT-LINE WINDS
## 523 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDE
## 524                                          SEVERE STORMS, LANDSLIDES, AND MUDSLIDES
## 525                 SEVERE STORMS AND FLOODING DUE TO THE REMNANTS OF HURRICANE SANDY
## 526                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 527                                                                          COVID-19
## 528                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 529                                                                          COVID-19
## 530                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 531                                                                          COVID-19
## 532                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 533                                                                 COVID-19 PANDEMIC
## 534                                                       FREEDOM AND NOBLE WILDFIRES
## 535                       SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 536                         SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 537                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 538                                                  SEVERE WINTER STORM AND FLOODING
## 539                         SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 540                                                 WILDFIRES AND STRAIGHT-LINE WINDS
## 541                          SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 542                                                                 COVID-19 PANDEMIC
## 543    SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 544                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 545                                                                   HURRICANE SANDY
## 546                                                         REMNANTS OF HURRICANE IDA
## 547                                                                 COVID-19 PANDEMIC
## 548                                                        SEVERE STORMS AND FLOODING
## 549                                                               SEVERE WINTER STORM
## 550                                                                       EARTHQUAKES
## 551                                                         SEVERE STORM AND FLOODING
## 552                                                                       EARTHQUAKES
## 553                                                   POTENTIAL TROPICAL CYCLONE NINE
## 554                                                                 COVID-19 PANDEMIC
## 555                                                             TROPICAL STORM DORIAN
## 556                                                                   HURRICANE MARIA
## 557                                                                    HURRICANE IRMA
## 558                                                                   HURRICANE MARIA
## 559                                                                   HURRICANE HENRI
## 560                                                 SEVERE WINTER STORM AND SNOWSTORM
## 561                                                                   HURRICANE SANDY
## 562                                                                   HURRICANE SANDY
## 563                                                                 COVID-19 PANDEMIC
## 564                                                                    HURRICANE IRMA
## 565                                                        SEVERE STORMS AND FLOODING
## 566                                                                HURRICANE FLORENCE
## 567                                                        SEVERE STORMS AND FLOODING
## 568                                                                  HURRICANE DORIAN
## 569                                                                 HURRICANE MATTHEW
## 570                                                                 HURRICANE MATTHEW
## 571                                                                 COVID-19 PANDEMIC
## 572                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 573                                                                 COVID-19 PANDEMIC
## 574                                      SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 575                                              SEVERE STORMS, TORNADO, AND FLOODING
## 576                                                 SEVERE WINTER STORM AND SNOWSTORM
## 577                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 578                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 579                                                               SEVERE WINTER STORM
## 580                                                                          COVID-19
## 581                                      SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 582                                                                         WILDFIRES
## 583                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 584                                                                 COVID-19 PANDEMIC
## 585                                                  SEVERE WINTER STORM AND FLOODING
## 586                                 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND
## 587                                                         SEVERE STORM AND FLOODING
## 588                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 589                                                              SEVERE WINTER STORMS
## 590                        SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 591                                                        SEVERE STORMS AND FLOODING
## 592                                                        SEVERE STORMS AND FLOODING
## 593                                                        SEVERE STORMS AND FLOODING
## 594                                                        SEVERE STORMS AND FLOODING
## 595                                                                   HURRICANE LAURA
## 596                                                                   HURRICANE HANNA
## 597                                            SEVERE STORMS, TORNADOES, AND FLOODING
## 598                                                                  HURRICANE HARVEY
## 599                                                                         EXPLOSION
## 600                                                        SEVERE STORMS AND FLOODING
## 601                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 602                SEVERE WINTER STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 603                                                                 COVID-19 PANDEMIC
## 604                                                                         EXPLOSION
## 605                                                                 COVID-19 PANDEMIC
## 606                                                                 HURRICANE MATTHEW
## 607                                                                HURRICANE FLORENCE
## 608                                                                   HURRICANE SANDY
## 609                                                                 COVID-19 PANDEMIC
## 610                                             SEVERE STORMS AND STRAIGHT-LINE WINDS
## 611                                                                HURRICANE FLORENCE
## 612                                                         SEVERE STORM AND FLOODING
## 613                                                                          COVID-19
## 614                                                                 COVID-19 PANDEMIC
## 615                                                        SEVERE STORMS AND FLOODING
## 616     SEVERE WINTER STORM, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 617                            SEVERE WINTER STORMS, FLOODING, LANDSLIDES,  MUDSLIDES
## 618                                                                 COVID-19 PANDEMIC
## 619           SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 620                                                           WILDFIRES AND MUDSLIDES
## 621                          SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 622                                                                         WILDFIRES
## 623                                                                         WILDFIRES
## 624                                                            FLOODING AND MUDSLIDES
## 625                                                                         WILDFIRES
## 626                                                            FLOODING AND MUDSLIDES
## 627                                                        SEVERE STORMS AND FLOODING
## 628           SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, FLOODING, AND LANDSLIDES
## 629                                            SEVERE STORMS, FLOODING, AND MUDSLIDES
## 630                                                                 COVID-19 PANDEMIC
## 631                                            SEVERE STORMS, FLOODING, AND MUDSLIDES
## 632                       SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 633                                  SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 634                                                        SEVERE STORMS AND FLOODING
## 635                                                                   HURRICANE SANDY
## 636                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 637                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 638                                SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 639                                                                   HURRICANE SANDY
## 640                                 SEVERE STORMS, FLOODING, MUDSLIDES, AND LANSLIDES
## 641                                                                 COVID-19 PANDEMIC
## 642                                             SEVERE STORMS AND STRAIGHT-LINE WINDS
## 643                     SEVERE STORMS, TORNADOES, FLOODING, MUDSLIDES, AND LANDSLIDES
## 644                          SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 645                                                                 COVID-19 PANDEMIC
## 646                                                        SEVERE STORMS AND FLOODING
##         incidentType IncidentYear IncidentMonth IncidentDuration ResponseDays
## 1              Flood         2013           May        25.000000   3099.04167
## 2         Biological         2020       January       660.000000    660.00000
## 3         Earthquake         2018      November         0.000000   1076.00000
## 4    Severe Storm(s)         2012     September        15.000000   3343.04167
## 5         Biological         2020       January       660.000000    660.00000
## 6    Severe Storm(s)         2018         March         1.000000   1332.04167
## 7          Hurricane         2020     September         2.000000    422.04167
## 8    Severe Storm(s)         2014         April         7.000000   2753.04167
## 9    Severe Storm(s)         2015           May        39.000000   2379.04167
## 10   Severe Storm(s)         2014         April         0.000000   2754.04167
## 11   Severe Storm(s)         2017         April        23.000000   1659.04167
## 12   Severe Storm(s)         2015      December        27.000000   2146.00000
## 13  Severe Ice Storm         2013      December         1.000000   2897.00000
## 14        Biological         2020       January       660.000000    660.00000
## 15             Flood         2019           May        24.000000    904.04167
## 16   Severe Storm(s)         2016         March         5.000000   2073.00000
## 17        Biological         2020       January       660.000000    660.00000
## 18        Biological         2020       January       660.000000    660.00000
## 19        Biological         2020       January       660.000000    660.00000
## 20        Biological         2020       January       660.000000    660.00000
## 21              Fire         2017      December        25.000000   1437.00000
## 22             Other         2017      February        16.000000   1737.00000
## 23   Severe Storm(s)         2019      February         2.000000   1001.00000
## 24        Biological         2020       January       660.000000    660.00000
## 25              Fire         2020     September        74.041667    432.04167
## 26        Earthquake         2019          July         8.000000    860.04167
## 27              Fire         2018          July        58.000000   1206.04167
## 28              Fire         2017      December        58.000000   1437.00000
## 29              Fire         2020        August        43.000000    453.04167
## 30              Fire         2018      November        17.000000   1098.00000
## 31              Fire         2021        August        88.041667     88.04167
## 32              Fire         2018          July        58.000000   1206.04167
## 33   Severe Storm(s)         2017       January         9.000000   1772.00000
## 34              Fire         2018      November        17.000000   1098.00000
## 35              Fire         2015     September        51.000000   2254.04167
## 36              Fire         2017       October        23.000000   1494.04167
## 37              Fire         2021          July       119.041667    119.04167
## 38             Flood         2017      February        22.000000   1743.00000
## 39             Flood         2015           May        43.000000   2382.04167
## 40              Fire         2012          June        32.000000   2307.00000
## 41        Biological         2020       January       660.000000    660.00000
## 42             Flood         2013     September        19.000000   1213.04167
## 43             Flood         2013     September        19.000000   2982.04167
## 44         Hurricane         2020        August         0.000000    463.04167
## 45        Biological         2020       January       660.000000    660.00000
## 46         Hurricane         2012       October        12.041667   3301.04167
## 47         Hurricane         2020        August         0.000000    463.04167
## 48   Severe Storm(s)         2013      February         3.000000   3026.95833
## 49         Hurricane         2012       October        12.041667   1678.00000
## 50        Biological         2020       January       660.000000    660.00000
## 51             Other         2021       January        13.000000    303.00000
## 52        Biological         2020       January       660.000000    660.00000
## 53         Hurricane         2019        August        12.000000    762.00000
## 54             Other         2021          June        10.000000    139.04167
## 55         Hurricane         2016       October        16.000000   1029.00000
## 56         Hurricane         2018       October        12.000000    676.00000
## 57   Severe Storm(s)         2014         April         8.000000   2753.04167
## 58         Hurricane         2016       October        16.000000   1864.04167
## 59         Hurricane         2020     September        14.000000    422.04167
## 60   Severe Storm(s)         2012          June        33.000000   3427.04167
## 61         Hurricane         2017     September        44.000000   1528.04167
## 62         Hurricane         2017     September        30.000000   1528.04167
## 63        Biological         2020       January       660.000000    660.00000
## 64         Hurricane         2018       October        12.000000   1130.04167
## 65         Hurricane         2017     September        44.000000    105.04167
## 66         Hurricane         2019        August         9.000000    761.00000
## 67   Severe Storm(s)         2017       January         0.000000   1773.00000
## 68        Biological         2020       January       660.000000    660.00000
## 69         Hurricane         2016       October        11.000000   1863.04167
## 70           Tornado         2017       January         1.000000   1754.00000
## 71         Hurricane         2017     September        13.000000   1525.04167
## 72         Hurricane         2018       October        14.000000   1128.04167
## 73         Hurricane         2016       October        11.000000    561.00000
## 74         Hurricane         2018        August         7.000000   1176.04167
## 75        Biological         2020       January       660.000000    660.00000
## 76             Other         2018           May       106.000000   1287.04167
## 77             Flood         2018         April         3.000000   1307.04167
## 78         Hurricane         2020          July         4.000000    475.04167
## 79   Severe Storm(s)         2020        August         0.000000    457.04167
## 80   Severe Storm(s)         2013           May        26.000000   1859.00000
## 81             Flood         2013         April        13.000000   1834.00000
## 82             Flood         2019         March        95.000000    974.04167
## 83        Biological         2020       January       660.000000    660.00000
## 84             Flood         2017         March        21.958333   1710.00000
## 85   Severe Storm(s)         2015      November         0.000000   1777.95833
## 86        Biological         2020       January       660.000000    660.00000
## 87             Flood         2017      February        22.000000   1739.00000
## 88        Biological         2020       January       660.000000    660.00000
## 89           Tornado         2013      November         0.000000   1850.00000
## 90             Flood         2019      February       128.958333    990.00000
## 91             Flood         2013         April        19.000000   3130.04167
## 92   Severe Storm(s)         2012      February         3.000000   2714.95833
## 93             Flood         2018      February        18.000000   1365.00000
## 94        Biological         2020       January       660.000000    660.00000
## 95        Biological         2020       January       660.000000    660.00000
## 96   Severe Storm(s)         2019           May        64.000000    916.04167
## 97   Severe Storm(s)         2012         April         1.000000   3497.04167
## 98             Flood         2015         March         5.958333   2444.00000
## 99        Biological         2020       January       660.000000    660.00000
## 100  Severe Storm(s)         2012      February         3.000000   3542.00000
## 101            Flood         2018      February         5.000000   1370.00000
## 102  Severe Storm(s)         2019      February        32.000000   1008.00000
## 103        Hurricane         2020       October         4.000000    400.04167
## 104        Hurricane         2020       October         3.000000    380.04167
## 105            Other         2019          July         5.000000    854.04167
## 106            Flood         2016        August        20.000000   1917.04167
## 107        Hurricane         2020        August         5.000000    242.00000
## 108        Hurricane         2012        August        15.000000   3363.04167
## 109        Hurricane         2021        August         8.000000     76.04167
## 110        Hurricane         2017       October         3.000000   1497.04167
## 111        Hurricane         2020        August         5.000000    445.04167
## 112        Hurricane         2012        August        15.000000   1025.00000
## 113        Hurricane         2020       October         3.000000    380.04167
## 114            Flood         2016         March        30.958333   2073.00000
## 115        Hurricane         2020     September         3.000000    423.04167
## 116        Hurricane         2017        August        14.000000    263.00000
## 117       Biological         2020       January       660.000000    660.00000
## 118        Hurricane         2020       October         4.000000    400.04167
## 119            Other         2013         April         7.000000   1430.00000
## 120  Severe Storm(s)         2018         March         1.000000   1349.00000
## 121        Hurricane         2012       October        12.041667    698.00000
## 122  Severe Storm(s)         2015       January         2.000000   2480.00000
## 123       Biological         2020       January       660.000000    660.00000
## 124            Other         2018         March         1.000000   1338.04167
## 125       Biological         2020       January       660.000000    660.00000
## 126  Severe Storm(s)         2017       October         3.000000   1473.04167
## 127            Other         2020           May         6.000000    543.04167
## 128       Biological         2020       January       660.000000    660.00000
## 129  Severe Storm(s)         2021          June         1.000000    138.04167
## 130            Flood         2013         April        28.000000   2723.00000
## 131            Flood         2014        August         2.000000   2648.04167
## 132  Severe Storm(s)         2017          June         5.000000   1602.04167
## 133       Biological         2020       January       660.000000    464.95833
## 134            Flood         2019         March        47.000000    974.04167
## 135  Severe Storm(s)         2013          June         6.000000   2580.00000
## 136            Flood         2016     September         3.000000   1876.04167
## 137  Severe Storm(s)         2013         April         2.000000   2443.04167
## 138            Flood         2018       October         2.000000   1128.04167
## 139  Severe Storm(s)         2012          June         7.000000   3436.04167
## 140       Biological         2020       January       660.000000    660.00000
## 141            Flood         2018          June        27.000000   1244.04167
## 142            Flood         2014          June        30.000000   2709.04167
## 143            Flood         2017         April        13.000000   1657.04167
## 144       Biological         2020       January       660.000000    660.00000
## 145            Flood         2015      December        18.000000   1987.95833
## 146  Severe Storm(s)         2015           May        73.000000   2250.00000
## 147            Flood         2015      December        17.000000   2149.00000
## 148            Flood         2019         March        36.000000    975.04167
## 149  Severe Storm(s)         2019         April        67.000000    926.04167
## 150  Severe Storm(s)         2014         April         5.000000   2753.04167
## 151        Hurricane         2012        August        16.000000   2832.00000
## 152  Severe Storm(s)         2020         April         0.000000    577.04167
## 153        Hurricane         2021        August         4.000000     74.04167
## 154          Tornado         2017       January         1.000000   1755.00000
## 155            Flood         2016         March        19.958333   2072.00000
## 156       Biological         2020       January       660.000000    660.00000
## 157  Severe Storm(s)         2015      December         5.000000   2149.00000
## 158            Flood         2013           May        15.000000   2689.00000
## 159       Biological         2020       January       660.000000    660.00000
## 160  Severe Storm(s)         2020      November         3.000000    363.00000
## 161        Hurricane         2016       October        20.000000    561.00000
## 162        Hurricane         2019     September         8.000000    319.00000
## 163        Hurricane         2019     September         8.000000    801.04167
## 164        Hurricane         2018     September        22.000000   1160.04167
## 165       Biological         2020       January       660.000000    660.00000
## 166        Hurricane         2016       October        20.000000   1863.04167
## 167            Flood         2013          July        10.000000   3052.04167
## 168        Hurricane         2018       October         2.000000   1127.04167
## 169            Flood         2014          June         6.000000   2050.04167
## 170            Flood         2017         March        37.000000   1693.04167
## 171            Flood         2013         April        15.000000   1037.04167
## 172       Biological         2020       January       660.000000    660.00000
## 173            Flood         2019         March        19.000000    973.04167
## 174  Severe Storm(s)         2014          June         7.000000   2699.00000
## 175       Biological         2020       January       660.000000    660.00000
## 176  Severe Storm(s)         2015           May        42.000000   2380.04167
## 177            Flood         2019         March       126.958333    977.00000
## 178            Other         2018         March         6.000000   1349.00000
## 179  Severe Storm(s)         2013      February         2.000000   2727.95833
## 180  Severe Storm(s)         2017       October         3.000000   1473.04167
## 181       Biological         2020       January       660.000000    660.00000
## 182        Hurricane         2012       October        13.041667   3302.04167
## 183       Biological         2020       January       660.000000    660.00000
## 184        Hurricane         2021     September         2.000000     70.04167
## 185            Flood         2017       October         2.000000   1498.04167
## 186  Severe Storm(s)         2013     September         3.000000   2980.04167
## 187       Biological         2020       January       660.000000    660.00000
## 188       Biological         2020       January       660.000000    660.00000
## 189  Severe Storm(s)         2017      February        17.000000   1739.00000
## 190  Severe Storm(s)         2017       January         9.000000   1770.00000
## 191        Hurricane         2021        August         3.000000     81.04167
## 192       Biological         2020       January       660.000000    660.00000
## 193        Hurricane         2012       October        12.041667   3301.04167
## 194            Flood         2013          June        14.000000   3059.04167
## 195        Hurricane         2021     September         2.000000     70.04167
## 196  Severe Storm(s)         2014           May         9.000000   2738.04167
## 197       Biological         2020       January       660.000000    660.00000
## 198  Severe Storm(s)         2012          June         3.000000    889.04167
## 199  Severe Storm(s)         2012          June         3.000000   2035.04167
## 200          Tornado         2019           May         2.000000    898.04167
## 201            Flood         2018      February        11.000000   1365.00000
## 202        Hurricane         2012       October         1.000000   2893.00000
## 203          Tornado         2013           May        15.000000   3098.04167
## 204       Biological         2020       January       660.000000    660.00000
## 205  Severe Storm(s)         2015           May        48.000000   2381.04167
## 206       Biological         2020       January       660.000000    660.00000
## 207          Tornado         2017           May         4.000000   1639.04167
## 208       Biological         2020       January       660.000000    660.00000
## 209  Severe Storm(s)         2017         April         4.000000   1657.04167
## 210       Biological         2020       January       660.000000    660.00000
## 211             Fire         2012        August        11.000000   2512.00000
## 212  Severe Storm(s)         2019           May        33.000000    918.04167
## 213  Severe Storm(s)         2019      February         3.000000    991.00000
## 214            Flood         2019         April        15.000000    949.04167
## 215  Severe Storm(s)         2016      December         3.000000   1792.00000
## 216  Severe Storm(s)         2017       January         3.000000   1768.00000
## 217             Fire         2020     September        57.041667    429.04167
## 218            Flood         2012       January         4.000000   2765.95833
## 219       Biological         2020       January       660.000000    660.00000
## 220  Severe Storm(s)         2015      December        17.000000   2166.00000
## 221  Severe Storm(s)         2013          June        15.000000   2045.04167
## 222        Hurricane         2012       October        13.041667    993.00000
## 223        Hurricane         2021        August         5.000000     71.04167
## 224       Biological         2020       January       660.000000    660.00000
## 225  Severe Storm(s)         2018        August         5.000000   1188.04167
## 226 Severe Ice Storm         2014      February        16.000000    477.95833
## 227       Earthquake         2019      December        38.000000    683.00000
## 228            Flood         2020     September         0.000000    423.04167
## 229       Earthquake         2019      December       187.958333    683.00000
## 230        Hurricane         2020          July         4.000000    471.04167
## 231       Biological         2020       January       660.000000    660.00000
## 232  Severe Storm(s)         2019        August        11.000000    807.04167
## 233        Hurricane         2017     September        59.041667   1515.04167
## 234        Hurricane         2017     September         2.000000   1527.04167
## 235        Hurricane         2017     September        59.041667    355.00000
## 236        Hurricane         2021        August         4.000000     82.04167
## 237  Severe Storm(s)         2013      February         1.000000   3197.00000
## 238        Hurricane         2012       October        13.041667    158.00000
## 239        Hurricane         2012       October         5.000000   3302.04167
## 240       Biological         2020       January       660.000000    660.00000
## 241        Hurricane         2017     September         7.000000    315.00000
## 242  Severe Storm(s)         2015       October        22.000000    809.04167
## 243        Hurricane         2018     September        30.000000   1159.04167
## 244            Flood         2015       October        22.000000   2232.04167
## 245        Hurricane         2019        August         6.000000    622.00000
## 246        Hurricane         2016       October        26.000000    442.04167
## 247        Hurricane         2016       October        26.000000   1863.04167
## 248       Biological         2020       January       660.000000    660.00000
## 249  Severe Storm(s)         2015           May        21.000000   2378.04167
## 250       Biological         2020       January       660.000000    660.00000
## 251  Severe Storm(s)         2013       October        13.000000   2960.04167
## 252  Severe Storm(s)         2013           May         7.000000   1586.00000
## 253  Severe Storm(s)         2013         April         2.000000   1632.00000
## 254  Severe Storm(s)         2015          June         7.000000   1668.04167
## 255            Flood         2014          June         7.000000   1930.00000
## 256  Severe Storm(s)         2016      December         2.000000   1782.00000
## 257       Biological         2020       January       660.000000    660.00000
## 258            Flood         2019         March        44.000000    973.04167
## 259             Fire         2016      November        11.000000   1808.00000
## 260          Tornado         2020         March         0.000000    617.00000
## 261       Biological         2020       January       660.000000    660.00000
## 262 Severe Ice Storm         2015      February         7.000000   2460.00000
## 263  Severe Storm(s)         2012      February         2.000000   1225.95833
## 264            Flood         2021        August         0.000000     81.04167
## 265  Severe Storm(s)         2014          June         5.000000   2498.00000
## 266 Severe Ice Storm         2021      February        10.000000    272.00000
## 267  Severe Storm(s)         2015           May        49.000000   2382.04167
## 268  Severe Storm(s)         2013       October         1.000000   2933.04167
## 269            Flood         2016         April        13.000000   2033.04167
## 270            Flood         2016           May        33.000000   1998.04167
## 271            Flood         2018     September        53.000000   1157.04167
## 272        Hurricane         2020        August         4.000000    444.04167
## 273        Hurricane         2020          July         6.000000    473.04167
## 274            Flood         2016         March        21.958333   2074.00000
## 275        Hurricane         2017        August        23.000000   1540.04167
## 276            Other         2013         April         3.000000   3129.04167
## 277            Flood         2018          June        24.000000   1240.04167
## 278  Severe Storm(s)         2015       October         9.000000   2211.04167
## 279  Severe Storm(s)         2015      December        26.000000   2146.00000
## 280       Biological         2020       January       660.000000    660.00000
## 281            Other         2013         April         3.000000    924.00000
## 282       Biological         2020       January       660.000000    660.00000
## 283        Hurricane         2016       October         8.000000   1860.04167
## 284        Hurricane         2018     September        13.000000   1159.04167
## 285        Hurricane         2012       October        13.041667   2860.00000
## 286       Biological         2020       January       660.000000    660.00000
## 287  Severe Storm(s)         2012          June         2.000000   2727.04167
## 288        Hurricane         2018     September         8.000000    697.00000
## 289  Severe Storm(s)         2017       October         1.000000   1473.04167
## 290       Biological         2020       January       660.000000    660.00000
## 291       Biological         2020       January       660.000000    660.00000
## 292            Flood         2019         April         0.000000    940.04167
## 293  Severe Storm(s)         2020      December        18.000000    316.00000
## 294            Flood         2017       January        23.000000   1745.00000
## 295       Biological         2020       January       660.000000    660.00000
## 296  Severe Storm(s)         2015      November         9.000000   2190.00000
## 297             Fire         2015        August        32.000000   2285.04167
## 298  Severe Storm(s)         2012       January         9.000000   3588.00000
## 299             Fire         2014          July        27.000000    653.00000
## 300             Fire         2015        August        28.000000    966.00000
## 301            Other         2014         March        37.000000   1370.04167
## 302             Fire         2014          July        27.000000   2681.04167
## 303            Other         2014         March        37.000000   2790.04167
## 304  Severe Storm(s)         2012          June         1.000000   2458.00000
## 305            Flood         2018        August        28.000000   1181.04167
## 306            Flood         2016     September         1.000000   1876.04167
## 307       Biological         2020       January       660.000000    660.00000
## 308  Severe Storm(s)         2013          June         8.000000   2885.00000
## 309            Flood         2019          July         2.000000    846.04167
## 310            Flood         2018          June         4.000000   1244.04167
## 311  Severe Storm(s)         2016          July         1.000000   1948.04167
## 312        Hurricane         2012       October        10.041667    899.00000
## 313            Flood         2016          June         7.000000   1967.04167
## 314            Flood         2015         April         2.000000   2403.04167
## 315            Other         2018      February         6.000000   1365.00000
## 316        Hurricane         2012       October        10.041667   2320.04167
## 317  Severe Storm(s)         2012         March        16.000000   2513.04167
## 318       Biological         2020       January       660.000000    660.00000
## 319  Severe Storm(s)         2012          June         9.000000   2727.04167
## 320  Severe Storm(s)         2012      February         5.000000   2267.95833
## 321  Severe Storm(s)         2015         March        10.958333   2444.00000
## 322       Biological         2020       January       660.000000    660.00000
## 323            Flood         2015           May        13.000000   2249.00000
## 324            Flood         2013           May        25.000000   3099.04167
## 325       Biological         2020       January       660.000000    660.00000
## 326       Earthquake         2018      November         0.000000   1076.00000
## 327  Severe Storm(s)         2012     September        15.000000   3343.04167
## 328       Biological         2020       January       660.000000    660.00000
## 329  Severe Storm(s)         2018         March         1.000000   1332.04167
## 330        Hurricane         2020     September         2.000000    422.04167
## 331  Severe Storm(s)         2014         April         7.000000   2753.04167
## 332  Severe Storm(s)         2015           May        39.000000   2379.04167
## 333  Severe Storm(s)         2014         April         0.000000   2754.04167
## 334  Severe Storm(s)         2017         April        23.000000   1659.04167
## 335  Severe Storm(s)         2015      December        27.000000   2146.00000
## 336 Severe Ice Storm         2013      December         1.000000   2897.00000
## 337       Biological         2020       January       660.000000    660.00000
## 338            Flood         2019           May        24.000000    904.04167
## 339  Severe Storm(s)         2016         March         5.000000   2073.00000
## 340       Biological         2020       January       660.000000    660.00000
## 341       Biological         2020       January       660.000000    660.00000
## 342       Biological         2020       January       660.000000    660.00000
## 343       Biological         2020       January       660.000000    660.00000
## 344             Fire         2017      December        25.000000   1437.00000
## 345            Other         2017      February        16.000000   1737.00000
## 346  Severe Storm(s)         2019      February         2.000000   1001.00000
## 347       Biological         2020       January       660.000000    660.00000
## 348             Fire         2020     September        74.041667    432.04167
## 349       Earthquake         2019          July         8.000000    860.04167
## 350             Fire         2018          July        58.000000   1206.04167
## 351             Fire         2017      December        58.000000   1437.00000
## 352             Fire         2020        August        43.000000    453.04167
## 353             Fire         2018      November        17.000000   1098.00000
## 354             Fire         2021        August        88.041667     88.04167
## 355             Fire         2018          July        58.000000   1206.04167
## 356  Severe Storm(s)         2017       January         9.000000   1772.00000
## 357             Fire         2018      November        17.000000   1098.00000
## 358             Fire         2015     September        51.000000   2254.04167
## 359             Fire         2017       October        23.000000   1494.04167
## 360             Fire         2021          July       119.041667    119.04167
## 361            Flood         2017      February        22.000000   1743.00000
## 362            Flood         2015           May        43.000000   2382.04167
## 363             Fire         2012          June        32.000000   2307.00000
## 364       Biological         2020       January       660.000000    660.00000
## 365            Flood         2013     September        19.000000   1213.04167
## 366            Flood         2013     September        19.000000   2982.04167
## 367        Hurricane         2020        August         0.000000    463.04167
## 368       Biological         2020       January       660.000000    660.00000
## 369        Hurricane         2012       October        12.041667   3301.04167
## 370        Hurricane         2020        August         0.000000    463.04167
## 371  Severe Storm(s)         2013      February         3.000000   3026.95833
## 372        Hurricane         2012       October        12.041667   1678.00000
## 373       Biological         2020       January       660.000000    660.00000
## 374            Other         2021       January        13.000000    303.00000
## 375       Biological         2020       January       660.000000    660.00000
## 376        Hurricane         2019        August        12.000000    762.00000
## 377            Other         2021          June        10.000000    139.04167
## 378        Hurricane         2016       October        16.000000   1029.00000
## 379        Hurricane         2018       October        12.000000    676.00000
## 380  Severe Storm(s)         2014         April         8.000000   2753.04167
## 381        Hurricane         2016       October        16.000000   1864.04167
## 382        Hurricane         2020     September        14.000000    422.04167
## 383  Severe Storm(s)         2012          June        33.000000   3427.04167
## 384        Hurricane         2017     September        44.000000   1528.04167
## 385        Hurricane         2017     September        30.000000   1528.04167
## 386       Biological         2020       January       660.000000    660.00000
## 387        Hurricane         2018       October        12.000000   1130.04167
## 388        Hurricane         2017     September        44.000000    105.04167
## 389        Hurricane         2019        August         9.000000    761.00000
## 390  Severe Storm(s)         2017       January         0.000000   1773.00000
## 391       Biological         2020       January       660.000000    660.00000
## 392        Hurricane         2016       October        11.000000   1863.04167
## 393          Tornado         2017       January         1.000000   1754.00000
## 394        Hurricane         2017     September        13.000000   1525.04167
## 395        Hurricane         2018       October        14.000000   1128.04167
## 396        Hurricane         2016       October        11.000000    561.00000
## 397        Hurricane         2018        August         7.000000   1176.04167
## 398       Biological         2020       January       660.000000    660.00000
## 399            Other         2018           May       106.000000   1287.04167
## 400            Flood         2018         April         3.000000   1307.04167
## 401        Hurricane         2020          July         4.000000    475.04167
## 402  Severe Storm(s)         2020        August         0.000000    457.04167
## 403  Severe Storm(s)         2013           May        26.000000   1859.00000
## 404            Flood         2013         April        13.000000   1834.00000
## 405            Flood         2019         March        95.000000    974.04167
## 406       Biological         2020       January       660.000000    660.00000
## 407            Flood         2017         March        21.958333   1710.00000
## 408  Severe Storm(s)         2015      November         0.000000   1777.95833
## 409       Biological         2020       January       660.000000    660.00000
## 410            Flood         2017      February        22.000000   1739.00000
## 411       Biological         2020       January       660.000000    660.00000
## 412          Tornado         2013      November         0.000000   1850.00000
## 413            Flood         2019      February       128.958333    990.00000
## 414            Flood         2013         April        19.000000   3130.04167
## 415  Severe Storm(s)         2012      February         3.000000   2714.95833
## 416            Flood         2018      February        18.000000   1365.00000
## 417       Biological         2020       January       660.000000    660.00000
## 418       Biological         2020       January       660.000000    660.00000
## 419  Severe Storm(s)         2019           May        64.000000    916.04167
## 420  Severe Storm(s)         2012         April         1.000000   3497.04167
## 421            Flood         2015         March         5.958333   2444.00000
## 422       Biological         2020       January       660.000000    660.00000
## 423  Severe Storm(s)         2012      February         3.000000   3542.00000
## 424            Flood         2018      February         5.000000   1370.00000
## 425  Severe Storm(s)         2019      February        32.000000   1008.00000
## 426        Hurricane         2020       October         4.000000    400.04167
## 427        Hurricane         2020       October         3.000000    380.04167
## 428            Other         2019          July         5.000000    854.04167
## 429            Flood         2016        August        20.000000   1917.04167
## 430        Hurricane         2020        August         5.000000    242.00000
## 431        Hurricane         2012        August        15.000000   3363.04167
## 432        Hurricane         2021        August         8.000000     76.04167
## 433        Hurricane         2017       October         3.000000   1497.04167
## 434        Hurricane         2020        August         5.000000    445.04167
## 435        Hurricane         2012        August        15.000000   1025.00000
## 436        Hurricane         2020       October         3.000000    380.04167
## 437            Flood         2016         March        30.958333   2073.00000
## 438        Hurricane         2020     September         3.000000    423.04167
## 439        Hurricane         2017        August        14.000000    263.00000
## 440       Biological         2020       January       660.000000    660.00000
## 441        Hurricane         2020       October         4.000000    400.04167
## 442            Other         2013         April         7.000000   1430.00000
## 443  Severe Storm(s)         2018         March         1.000000   1349.00000
## 444        Hurricane         2012       October        12.041667    698.00000
## 445  Severe Storm(s)         2015       January         2.000000   2480.00000
## 446       Biological         2020       January       660.000000    660.00000
## 447            Other         2018         March         1.000000   1338.04167
## 448       Biological         2020       January       660.000000    660.00000
## 449  Severe Storm(s)         2017       October         3.000000   1473.04167
## 450            Other         2020           May         6.000000    543.04167
## 451       Biological         2020       January       660.000000    660.00000
## 452  Severe Storm(s)         2021          June         1.000000    138.04167
## 453            Flood         2013         April        28.000000   2723.00000
## 454            Flood         2014        August         2.000000   2648.04167
## 455  Severe Storm(s)         2017          June         5.000000   1602.04167
## 456       Biological         2020       January       660.000000    464.95833
## 457            Flood         2019         March        47.000000    974.04167
## 458  Severe Storm(s)         2013          June         6.000000   2580.00000
## 459            Flood         2016     September         3.000000   1876.04167
## 460  Severe Storm(s)         2013         April         2.000000   2443.04167
## 461            Flood         2018       October         2.000000   1128.04167
## 462  Severe Storm(s)         2012          June         7.000000   3436.04167
## 463       Biological         2020       January       660.000000    660.00000
## 464            Flood         2018          June        27.000000   1244.04167
## 465            Flood         2014          June        30.000000   2709.04167
## 466            Flood         2017         April        13.000000   1657.04167
## 467       Biological         2020       January       660.000000    660.00000
## 468            Flood         2015      December        18.000000   1987.95833
## 469  Severe Storm(s)         2015           May        73.000000   2250.00000
## 470            Flood         2015      December        17.000000   2149.00000
## 471            Flood         2019         March        36.000000    975.04167
## 472  Severe Storm(s)         2019         April        67.000000    926.04167
## 473  Severe Storm(s)         2014         April         5.000000   2753.04167
## 474        Hurricane         2012        August        16.000000   2832.00000
## 475  Severe Storm(s)         2020         April         0.000000    577.04167
## 476        Hurricane         2021        August         4.000000     74.04167
## 477          Tornado         2017       January         1.000000   1755.00000
## 478            Flood         2016         March        19.958333   2072.00000
## 479       Biological         2020       January       660.000000    660.00000
## 480  Severe Storm(s)         2015      December         5.000000   2149.00000
## 481            Flood         2013           May        15.000000   2689.00000
## 482       Biological         2020       January       660.000000    660.00000
## 483  Severe Storm(s)         2020      November         3.000000    363.00000
## 484        Hurricane         2016       October        20.000000    561.00000
## 485        Hurricane         2019     September         8.000000    319.00000
## 486        Hurricane         2019     September         8.000000    801.04167
## 487        Hurricane         2018     September        22.000000   1160.04167
## 488       Biological         2020       January       660.000000    660.00000
## 489        Hurricane         2016       October        20.000000   1863.04167
## 490            Flood         2013          July        10.000000   3052.04167
## 491        Hurricane         2018       October         2.000000   1127.04167
## 492            Flood         2014          June         6.000000   2050.04167
## 493            Flood         2017         March        37.000000   1693.04167
## 494            Flood         2013         April        15.000000   1037.04167
## 495       Biological         2020       January       660.000000    660.00000
## 496            Flood         2019         March        19.000000    973.04167
## 497  Severe Storm(s)         2014          June         7.000000   2699.00000
## 498       Biological         2020       January       660.000000    660.00000
## 499  Severe Storm(s)         2015           May        42.000000   2380.04167
## 500            Flood         2019         March       126.958333    977.00000
## 501            Other         2018         March         6.000000   1349.00000
## 502  Severe Storm(s)         2013      February         2.000000   2727.95833
## 503  Severe Storm(s)         2017       October         3.000000   1473.04167
## 504       Biological         2020       January       660.000000    660.00000
## 505        Hurricane         2012       October        13.041667   3302.04167
## 506       Biological         2020       January       660.000000    660.00000
## 507        Hurricane         2021     September         2.000000     70.04167
## 508            Flood         2017       October         2.000000   1498.04167
## 509  Severe Storm(s)         2013     September         3.000000   2980.04167
## 510       Biological         2020       January       660.000000    660.00000
## 511       Biological         2020       January       660.000000    660.00000
## 512  Severe Storm(s)         2017      February        17.000000   1739.00000
## 513  Severe Storm(s)         2017       January         9.000000   1770.00000
## 514        Hurricane         2021        August         3.000000     81.04167
## 515       Biological         2020       January       660.000000    660.00000
## 516        Hurricane         2012       October        12.041667   3301.04167
## 517            Flood         2013          June        14.000000   3059.04167
## 518        Hurricane         2021     September         2.000000     70.04167
## 519  Severe Storm(s)         2014           May         9.000000   2738.04167
## 520       Biological         2020       January       660.000000    660.00000
## 521  Severe Storm(s)         2012          June         3.000000    889.04167
## 522  Severe Storm(s)         2012          June         3.000000   2035.04167
## 523          Tornado         2019           May         2.000000    898.04167
## 524            Flood         2018      February        11.000000   1365.00000
## 525        Hurricane         2012       October         1.000000   2893.00000
## 526          Tornado         2013           May        15.000000   3098.04167
## 527       Biological         2020       January       660.000000    660.00000
## 528  Severe Storm(s)         2015           May        48.000000   2381.04167
## 529       Biological         2020       January       660.000000    660.00000
## 530          Tornado         2017           May         4.000000   1639.04167
## 531       Biological         2020       January       660.000000    660.00000
## 532  Severe Storm(s)         2017         April         4.000000   1657.04167
## 533       Biological         2020       January       660.000000    660.00000
## 534             Fire         2012        August        11.000000   2512.00000
## 535  Severe Storm(s)         2019           May        33.000000    918.04167
## 536  Severe Storm(s)         2019      February         3.000000    991.00000
## 537            Flood         2019         April        15.000000    949.04167
## 538  Severe Storm(s)         2016      December         3.000000   1792.00000
## 539  Severe Storm(s)         2017       January         3.000000   1768.00000
## 540             Fire         2020     September        57.041667    429.04167
## 541            Flood         2012       January         4.000000   2765.95833
## 542       Biological         2020       January       660.000000    660.00000
## 543  Severe Storm(s)         2015      December        17.000000   2166.00000
## 544  Severe Storm(s)         2013          June        15.000000   2045.04167
## 545        Hurricane         2012       October        13.041667    993.00000
## 546        Hurricane         2021        August         5.000000     71.04167
## 547       Biological         2020       January       660.000000    660.00000
## 548  Severe Storm(s)         2018        August         5.000000   1188.04167
## 549 Severe Ice Storm         2014      February        16.000000    477.95833
## 550       Earthquake         2019      December        38.000000    683.00000
## 551            Flood         2020     September         0.000000    423.04167
## 552       Earthquake         2019      December       187.958333    683.00000
## 553        Hurricane         2020          July         4.000000    471.04167
## 554       Biological         2020       January       660.000000    660.00000
## 555  Severe Storm(s)         2019        August        11.000000    807.04167
## 556        Hurricane         2017     September        59.041667   1515.04167
## 557        Hurricane         2017     September         2.000000   1527.04167
## 558        Hurricane         2017     September        59.041667    355.00000
## 559        Hurricane         2021        August         4.000000     82.04167
## 560  Severe Storm(s)         2013      February         1.000000   3197.00000
## 561        Hurricane         2012       October        13.041667    158.00000
## 562        Hurricane         2012       October         5.000000   3302.04167
## 563       Biological         2020       January       660.000000    660.00000
## 564        Hurricane         2017     September         7.000000    315.00000
## 565  Severe Storm(s)         2015       October        22.000000    809.04167
## 566        Hurricane         2018     September        30.000000   1159.04167
## 567            Flood         2015       October        22.000000   2232.04167
## 568        Hurricane         2019        August         6.000000    622.00000
## 569        Hurricane         2016       October        26.000000    442.04167
## 570        Hurricane         2016       October        26.000000   1863.04167
## 571       Biological         2020       January       660.000000    660.00000
## 572  Severe Storm(s)         2015           May        21.000000   2378.04167
## 573       Biological         2020       January       660.000000    660.00000
## 574  Severe Storm(s)         2013       October        13.000000   2960.04167
## 575  Severe Storm(s)         2013           May         7.000000   1586.00000
## 576  Severe Storm(s)         2013         April         2.000000   1632.00000
## 577  Severe Storm(s)         2015          June         7.000000   1668.04167
## 578            Flood         2014          June         7.000000   1930.00000
## 579  Severe Storm(s)         2016      December         2.000000   1782.00000
## 580       Biological         2020       January       660.000000    660.00000
## 581            Flood         2019         March        44.000000    973.04167
## 582             Fire         2016      November        11.000000   1808.00000
## 583          Tornado         2020         March         0.000000    617.00000
## 584       Biological         2020       January       660.000000    660.00000
## 585 Severe Ice Storm         2015      February         7.000000   2460.00000
## 586  Severe Storm(s)         2012      February         2.000000   1225.95833
## 587            Flood         2021        August         0.000000     81.04167
## 588  Severe Storm(s)         2014          June         5.000000   2498.00000
## 589 Severe Ice Storm         2021      February        10.000000    272.00000
## 590  Severe Storm(s)         2015           May        49.000000   2382.04167
## 591  Severe Storm(s)         2013       October         1.000000   2933.04167
## 592            Flood         2016         April        13.000000   2033.04167
## 593            Flood         2016           May        33.000000   1998.04167
## 594            Flood         2018     September        53.000000   1157.04167
## 595        Hurricane         2020        August         4.000000    444.04167
## 596        Hurricane         2020          July         6.000000    473.04167
## 597            Flood         2016         March        21.958333   2074.00000
## 598        Hurricane         2017        August        23.000000   1540.04167
## 599            Other         2013         April         3.000000   3129.04167
## 600            Flood         2018          June        24.000000   1240.04167
## 601  Severe Storm(s)         2015       October         9.000000   2211.04167
## 602  Severe Storm(s)         2015      December        26.000000   2146.00000
## 603       Biological         2020       January       660.000000    660.00000
## 604            Other         2013         April         3.000000    924.00000
## 605       Biological         2020       January       660.000000    660.00000
## 606        Hurricane         2016       October         8.000000   1860.04167
## 607        Hurricane         2018     September        13.000000   1159.04167
## 608        Hurricane         2012       October        13.041667   2860.00000
## 609       Biological         2020       January       660.000000    660.00000
## 610  Severe Storm(s)         2012          June         2.000000   2727.04167
## 611        Hurricane         2018     September         8.000000    697.00000
## 612  Severe Storm(s)         2017       October         1.000000   1473.04167
## 613       Biological         2020       January       660.000000    660.00000
## 614       Biological         2020       January       660.000000    660.00000
## 615            Flood         2019         April         0.000000    940.04167
## 616  Severe Storm(s)         2020      December        18.000000    316.00000
## 617            Flood         2017       January        23.000000   1745.00000
## 618       Biological         2020       January       660.000000    660.00000
## 619  Severe Storm(s)         2015      November         9.000000   2190.00000
## 620             Fire         2015        August        32.000000   2285.04167
## 621  Severe Storm(s)         2012       January         9.000000   3588.00000
## 622             Fire         2014          July        27.000000    653.00000
## 623             Fire         2015        August        28.000000    966.00000
## 624            Other         2014         March        37.000000   1370.04167
## 625             Fire         2014          July        27.000000   2681.04167
## 626            Other         2014         March        37.000000   2790.04167
## 627  Severe Storm(s)         2012          June         1.000000   2458.00000
## 628            Flood         2018        August        28.000000   1181.04167
## 629            Flood         2016     September         1.000000   1876.04167
## 630       Biological         2020       January       660.000000    660.00000
## 631  Severe Storm(s)         2013          June         8.000000   2885.00000
## 632            Flood         2019          July         2.000000    846.04167
## 633            Flood         2018          June         4.000000   1244.04167
## 634  Severe Storm(s)         2016          July         1.000000   1948.04167
## 635        Hurricane         2012       October        10.041667    899.00000
## 636            Flood         2016          June         7.000000   1967.04167
## 637            Flood         2015         April         2.000000   2403.04167
## 638            Other         2018      February         6.000000   1365.00000
## 639        Hurricane         2012       October        10.041667   2320.04167
## 640  Severe Storm(s)         2012         March        16.000000   2513.04167
## 641       Biological         2020       January       660.000000    660.00000
## 642  Severe Storm(s)         2012          June         9.000000   2727.04167
## 643  Severe Storm(s)         2012      February         5.000000   2267.95833
## 644  Severe Storm(s)         2015         March        10.958333   2444.00000
## 645       Biological         2020       January       660.000000    660.00000
## 646            Flood         2015           May        13.000000   2249.00000
##              IH       IH_pct          PA       PA_pct          HM       HM_pct
## 1       Awarded  0.117647059     Awarded  0.147058824     Awarded  0.147058824
## 2       Awarded  2.470588235     Awarded  2.470588235     Awarded  1.000000000
## 3       Awarded  0.088235294     Awarded  0.088235294     Awarded  0.088235294
## 4   Not Awarded  0.000000000     Awarded  0.147058824     Awarded  0.147058824
## 5       Awarded  1.029850746     Awarded  1.029850746     Awarded  1.029850746
## 6       Awarded  0.044776119     Awarded  0.059701493     Awarded  0.059701493
## 7       Awarded  0.044776119     Awarded  0.223880597     Awarded  0.223880597
## 8       Awarded  0.134328358     Awarded  0.313432836     Awarded  0.313432836
## 9       Awarded  0.120000000     Awarded  0.373333333     Awarded  0.386666667
## 10      Awarded  0.053333333     Awarded  0.146666667     Awarded  0.160000000
## 11      Awarded  0.213333333     Awarded  0.400000000     Awarded  0.426666667
## 12      Awarded  0.146666667     Awarded  0.426666667     Awarded  0.506666667
## 13  Not Awarded  0.000000000     Awarded  0.186666667     Awarded  0.186666667
## 14      Awarded  1.013333333     Awarded  1.013333333     Awarded  1.013333333
## 15      Awarded  0.173333333     Awarded  0.226666667     Awarded  0.213333333
## 16  Not Awarded  0.000000000     Awarded  0.160000000     Awarded  0.160000000
## 17  Not Awarded  0.000000000     Awarded  2.400000000 Not Awarded  0.000000000
## 18      Awarded  2.400000000     Awarded  2.400000000     Awarded  2.400000000
## 19      Awarded  0.066666667     Awarded  0.066666667     Awarded  0.066666667
## 20      Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 21  Not Awarded  0.000000000     Awarded  0.086206897 Not Awarded  0.000000000
## 22  Not Awarded  0.000000000     Awarded  0.051724138 Not Awarded  0.000000000
## 23  Not Awarded  0.000000000     Awarded  0.189655172     Awarded  0.189655172
## 24  Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 25      Awarded  0.172413793     Awarded  0.206896552     Awarded  0.206896552
## 26  Not Awarded  0.000000000     Awarded  0.034482759 Not Awarded  0.000000000
## 27      Awarded  0.034482759     Awarded  0.034482759     Awarded  0.034482759
## 28      Awarded  0.068965517     Awarded  0.034482759     Awarded  0.068965517
## 29      Awarded  0.258620690     Awarded  0.344827586     Awarded  0.327586207
## 30      Awarded  0.051724138     Awarded  0.051724138     Awarded  0.051724138
## 31  Not Awarded  0.000000000     Awarded  0.017241379     Awarded  0.017241379
## 32  Not Awarded  0.000000000     Awarded  0.017241379 Not Awarded  0.000000000
## 33  Not Awarded  0.000000000     Awarded  0.586206897     Awarded  0.586206897
## 34  Not Awarded  0.000000000     Awarded  0.051724138 Not Awarded  0.000000000
## 35      Awarded  0.034482759     Awarded  0.034482759     Awarded  0.034482759
## 36      Awarded  0.137931034     Awarded  0.155172414     Awarded  0.155172414
## 37      Awarded  0.103448276     Awarded  0.103448276     Awarded  0.103448276
## 38  Not Awarded  0.000000000     Awarded  0.758620690     Awarded  0.758620690
## 39  Not Awarded  0.000000000     Awarded  0.234375000     Awarded  0.234375000
## 40      Awarded  0.031250000     Awarded  0.046875000     Awarded  0.046875000
## 41      Awarded  1.031250000     Awarded  1.031250000     Awarded  1.031250000
## 42  Not Awarded  0.000000000     Awarded  0.234375000 Not Awarded  0.000000000
## 43      Awarded  0.171875000     Awarded  0.281250000     Awarded  0.281250000
## 44  Not Awarded  0.000000000     Awarded  1.250000000 Not Awarded  0.000000000
## 45      Awarded  1.500000000     Awarded  1.500000000     Awarded  1.500000000
## 46      Awarded  0.625000000     Awarded  1.000000000     Awarded  1.000000000
## 47  Not Awarded  0.000000000     Awarded  1.250000000     Awarded  1.250000000
## 48  Not Awarded  0.000000000     Awarded  1.125000000     Awarded  1.125000000
## 49  Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 50      Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 51  Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 52      Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 53  Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 54      Awarded  0.014925373     Awarded  0.014925373 Not Awarded  0.000000000
## 55  Not Awarded  0.000000000     Awarded  0.417910448 Not Awarded  0.000000000
## 56  Not Awarded  0.000000000     Awarded  0.522388060 Not Awarded  0.000000000
## 57      Awarded  0.074626866     Awarded  0.134328358     Awarded  0.134328358
## 58      Awarded  0.134328358     Awarded  0.268656716     Awarded  0.268656716
## 59      Awarded  0.074626866     Awarded  0.208955224     Awarded  0.208955224
## 60      Awarded  0.328358209     Awarded  0.447761194     Awarded  0.507462687
## 61      Awarded  0.731343284     Awarded  1.000000000     Awarded  1.000000000
## 62      Awarded  0.089552239     Awarded  0.089552239     Awarded  0.089552239
## 63      Awarded  1.044776119     Awarded  1.044776119     Awarded  1.044776119
## 64      Awarded  0.179104478     Awarded  0.268656716     Awarded  0.238805970
## 65  Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 66  Not Awarded  0.000000000     Awarded  0.075471698 Not Awarded  0.000000000
## 67      Awarded  0.006289308     Awarded  0.044025157     Awarded  0.044025157
## 68      Awarded  1.006289308     Awarded  1.006289308     Awarded  1.006289308
## 69      Awarded  0.062893082     Awarded  0.125786164     Awarded  0.125786164
## 70      Awarded  0.050314465     Awarded  0.144654088     Awarded  0.144654088
## 71      Awarded  0.044025157     Awarded  1.000000000     Awarded  1.000000000
## 72      Awarded  0.125786164     Awarded  0.433962264     Awarded  0.433962264
## 73  Not Awarded  0.000000000     Awarded  0.188679245 Not Awarded  0.000000000
## 74  Not Awarded  0.000000000     Awarded  0.800000000 Not Awarded  0.000000000
## 75      Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 76      Awarded  0.200000000     Awarded  0.200000000     Awarded  0.200000000
## 77      Awarded  0.400000000     Awarded  0.400000000     Awarded  0.400000000
## 78  Not Awarded  0.000000000     Awarded  0.800000000 Not Awarded  0.000000000
## 79      Awarded  0.121212121     Awarded  0.232323232     Awarded  0.232323232
## 80  Not Awarded  0.000000000     Awarded  0.494949495     Awarded  0.494949495
## 81  Not Awarded  0.000000000     Awarded  0.202020202     Awarded  0.202020202
## 82      Awarded  0.101010101     Awarded  0.808080808     Awarded  0.808080808
## 83      Awarded  1.020202020     Awarded  1.020202020     Awarded  1.020202020
## 84  Not Awarded  0.000000000     Awarded  0.204545455     Awarded  0.204545455
## 85  Not Awarded  0.000000000     Awarded  0.113636364     Awarded  0.113636364
## 86      Awarded  1.113636364     Awarded  1.000000000     Awarded  1.000000000
## 87  Not Awarded  0.000000000     Awarded  0.250000000     Awarded  0.250000000
## 88      Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 89      Awarded  0.147058824 Not Awarded  0.000000000     Awarded  0.147058824
## 90  Not Awarded  0.000000000     Awarded  0.274509804     Awarded  0.274509804
## 91      Awarded  0.343137255     Awarded  0.392156863     Awarded  0.460784314
## 92      Awarded  0.065217391     Awarded  0.054347826     Awarded  0.065217391
## 93      Awarded  0.239130435     Awarded  0.304347826     Awarded  0.336956522
## 94      Awarded  1.010869565     Awarded  1.010869565     Awarded  1.010869565
## 95      Awarded  1.152380952     Awarded  1.152380952     Awarded  1.152380952
## 96  Not Awarded  0.000000000     Awarded  0.314285714 Not Awarded  0.000000000
## 97  Not Awarded  0.000000000     Awarded  0.133333333     Awarded  0.133333333
## 98  Not Awarded  0.000000000     Awarded  0.491666667     Awarded  0.491666667
## 99      Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 100     Awarded  0.175000000     Awarded  0.133333333     Awarded  0.191666667
## 101 Not Awarded  0.000000000     Awarded  0.183333333     Awarded  0.183333333
## 102 Not Awarded  0.000000000     Awarded  0.508333333     Awarded  0.508333333
## 103     Awarded  0.187500000     Awarded  1.000000000     Awarded  1.000000000
## 104 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 105 Not Awarded  0.000000000     Awarded  0.640625000 Not Awarded  0.000000000
## 106     Awarded  0.343750000     Awarded  0.406250000     Awarded  0.406250000
## 107 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 108     Awarded  0.406250000     Awarded  0.859375000     Awarded  0.859375000
## 109     Awarded  0.390625000     Awarded  1.000000000     Awarded  1.000000000
## 110 Not Awarded  0.000000000     Awarded  0.296875000 Not Awarded  0.000000000
## 111     Awarded  0.328125000     Awarded  1.000000000     Awarded  1.000000000
## 112 Not Awarded  0.000000000     Awarded  0.531250000 Not Awarded  0.000000000
## 113     Awarded  0.093750000     Awarded  0.546875000     Awarded  0.546875000
## 114     Awarded  0.562500000     Awarded  0.578125000     Awarded  0.578125000
## 115 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 116 Not Awarded  0.000000000     Awarded  0.187500000 Not Awarded  0.000000000
## 117     Awarded  1.109375000     Awarded  1.109375000     Awarded  1.109375000
## 118 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 119 Not Awarded  0.000000000     Awarded  0.285714286 Not Awarded  0.000000000
## 120 Not Awarded  0.000000000     Awarded  0.428571429     Awarded  0.428571429
## 121 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 122 Not Awarded  0.000000000     Awarded  0.714285714     Awarded  0.714285714
## 123     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.214285714
## 124 Not Awarded  0.000000000     Awarded  0.357142857     Awarded  0.357142857
## 125     Awarded 27.687500000     Awarded 27.312500000     Awarded 27.250000000
## 126 Not Awarded  0.000000000     Awarded  0.812500000     Awarded  0.812500000
## 127     Awarded  0.060240964     Awarded  0.060240964     Awarded  0.060240964
## 128     Awarded  1.132530120     Awarded  1.132530120     Awarded  1.132530120
## 129     Awarded  0.048192771 Not Awarded  0.000000000     Awarded  0.048192771
## 130 Not Awarded  0.000000000     Awarded  0.192771084     Awarded  0.192771084
## 131     Awarded  0.036144578     Awarded  0.036144578     Awarded  0.036144578
## 132     Awarded  0.048192771 Not Awarded  0.000000000     Awarded  0.048192771
## 133 Not Awarded  0.000000000     Awarded  1.206896552 Not Awarded  0.000000000
## 134 Not Awarded  0.000000000     Awarded  0.632183908     Awarded  0.632183908
## 135 Not Awarded  0.000000000     Awarded  0.206896552     Awarded  0.206896552
## 136     Awarded  0.080459770     Awarded  0.103448276     Awarded  0.114942529
## 137 Not Awarded  0.000000000     Awarded  0.057471264     Awarded  0.057471264
## 138 Not Awarded  0.000000000     Awarded  0.011494253     Awarded  0.011494253
## 139 Not Awarded  0.000000000     Awarded  0.206896552     Awarded  0.206896552
## 140     Awarded  1.229885057     Awarded  1.229885057     Awarded  1.229885057
## 141 Not Awarded  0.000000000     Awarded  0.356321839     Awarded  0.356321839
## 142 Not Awarded  0.000000000     Awarded  0.459770115     Awarded  0.459770115
## 143     Awarded  0.304347826     Awarded  0.460869565     Awarded  0.478260870
## 144     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 145 Not Awarded  0.000000000     Awarded  0.643478261 Not Awarded  0.000000000
## 146 Not Awarded  0.000000000     Awarded  0.660869565     Awarded  0.660869565
## 147     Awarded  0.286956522     Awarded  0.365217391     Awarded  0.452173913
## 148 Not Awarded  0.000000000     Awarded  0.139130435     Awarded  0.139130435
## 149     Awarded  0.226086957     Awarded  0.721739130     Awarded  0.756521739
## 150     Awarded  0.146341463     Awarded  0.121951220     Awarded  0.158536585
## 151     Awarded  0.268292683     Awarded  0.597560976     Awarded  0.597560976
## 152     Awarded  0.109756098     Awarded  0.341463415     Awarded  0.353658537
## 153 Not Awarded  0.000000000     Awarded  1.012195122 Not Awarded  0.000000000
## 154     Awarded  0.048780488     Awarded  0.036585366     Awarded  0.048780488
## 155     Awarded  0.207317073     Awarded  0.341463415     Awarded  0.353658537
## 156     Awarded  1.012195122     Awarded  1.012195122     Awarded  1.012195122
## 157     Awarded  0.097560976     Awarded  0.146341463     Awarded  0.134146341
## 158 Not Awarded  0.000000000     Awarded  0.267857143     Awarded  0.267857143
## 159     Awarded  1.142857143     Awarded  1.142857143     Awarded  1.142857143
## 160 Not Awarded  0.000000000     Awarded  0.190000000     Awarded  0.190000000
## 161 Not Awarded  0.000000000     Awarded  0.660000000 Not Awarded  0.000000000
## 162 Not Awarded  0.000000000     Awarded  1.010000000 Not Awarded  0.000000000
## 163 Not Awarded  0.000000000     Awarded  0.280000000     Awarded  0.260000000
## 164     Awarded  0.340000000     Awarded  0.510000000     Awarded  0.520000000
## 165     Awarded  1.060000000     Awarded  1.060000000     Awarded  1.060000000
## 166     Awarded  0.450000000     Awarded  0.500000000     Awarded  0.500000000
## 167 Not Awarded  0.000000000     Awarded  0.140000000     Awarded  0.140000000
## 168 Not Awarded  0.000000000     Awarded  0.210000000     Awarded  0.210000000
## 169 Not Awarded  0.000000000     Awarded  0.188679245     Awarded  0.188679245
## 170 Not Awarded  0.000000000     Awarded  0.207547170     Awarded  0.207547170
## 171 Not Awarded  0.000000000     Awarded  0.113207547 Not Awarded  0.000000000
## 172     Awarded  1.094339623     Awarded  1.094339623     Awarded  1.094339623
## 173 Not Awarded  0.000000000     Awarded  0.021505376     Awarded  0.021505376
## 174 Not Awarded  0.000000000     Awarded  0.129032258     Awarded  0.129032258
## 175     Awarded  1.086021505     Awarded  1.086021505     Awarded  1.086021505
## 176 Not Awarded  0.000000000     Awarded  0.301075269     Awarded  0.301075269
## 177     Awarded  0.322580645     Awarded  0.956989247     Awarded  0.956989247
## 178 Not Awarded  0.000000000     Awarded  0.100000000     Awarded  0.100000000
## 179 Not Awarded  0.000000000     Awarded  0.800000000     Awarded  0.800000000
## 180 Not Awarded  0.000000000     Awarded  0.600000000     Awarded  0.600000000
## 181     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 182     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 183     Awarded  1.095238095     Awarded  1.095238095     Awarded  1.095238095
## 184     Awarded  0.571428571     Awarded  1.000000000     Awarded  1.000000000
## 185 Not Awarded  0.000000000     Awarded  0.030303030     Awarded  0.030303030
## 186 Not Awarded  0.000000000     Awarded  0.030303030     Awarded  0.030303030
## 187     Awarded  2.090909091     Awarded  2.090909091     Awarded  2.090909091
## 188     Awarded  2.588235294     Awarded  2.588235294     Awarded  2.588235294
## 189 Not Awarded  0.000000000     Awarded  0.411764706     Awarded  0.411764706
## 190 Not Awarded  0.000000000     Awarded  0.470588235     Awarded  0.470588235
## 191 Not Awarded  0.000000000     Awarded  0.419354839 Not Awarded  0.000000000
## 192     Awarded  1.032258065     Awarded  1.032258065     Awarded  1.032258065
## 193     Awarded  0.209677419     Awarded  0.225806452     Awarded  0.225806452
## 194 Not Awarded  0.000000000     Awarded  0.258064516     Awarded  0.258064516
## 195     Awarded  0.145161290     Awarded  0.225806452     Awarded  0.225806452
## 196 Not Awarded  0.000000000     Awarded  0.177419355     Awarded  0.177419355
## 197     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 198 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 199 Not Awarded  0.000000000     Awarded  0.443181818     Awarded  0.443181818
## 200     Awarded  0.125000000     Awarded  0.045454545     Awarded  0.136363636
## 201 Not Awarded  0.000000000     Awarded  0.250000000     Awarded  0.250000000
## 202 Not Awarded  0.000000000     Awarded  0.022727273     Awarded  0.022727273
## 203     Awarded  0.116883117     Awarded  0.259740260     Awarded  0.272727273
## 204 Not Awarded  0.000000000     Awarded  0.012987013 Not Awarded  0.000000000
## 205     Awarded  0.584415584     Awarded  0.766233766     Awarded  0.818181818
## 206 Not Awarded  0.000000000     Awarded  0.012987013 Not Awarded  0.000000000
## 207 Not Awarded  0.000000000     Awarded  0.207792208     Awarded  0.207792208
## 208 Not Awarded  0.000000000     Awarded  0.012987013 Not Awarded  0.000000000
## 209 Not Awarded  0.000000000     Awarded  0.272727273     Awarded  0.272727273
## 210     Awarded  1.454545455     Awarded  1.454545455     Awarded  1.454545455
## 211     Awarded  0.025974026 Not Awarded  0.000000000     Awarded  0.025974026
## 212     Awarded  0.350649351     Awarded  0.662337662     Awarded  0.662337662
## 213 Not Awarded  0.000000000     Awarded  0.138888889     Awarded  0.138888889
## 214 Not Awarded  0.000000000     Awarded  0.166666667     Awarded  0.166666667
## 215 Not Awarded  0.000000000     Awarded  0.055555556     Awarded  0.055555556
## 216 Not Awarded  0.000000000     Awarded  0.111111111     Awarded  0.111111111
## 217     Awarded  0.222222222     Awarded  0.555555556     Awarded  0.555555556
## 218 Not Awarded  0.000000000     Awarded  0.333333333     Awarded  0.333333333
## 219     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.305555556
## 220 Not Awarded  0.000000000     Awarded  0.388888889     Awarded  0.388888889
## 221 Not Awarded  0.000000000     Awarded  0.164179104     Awarded  0.164179104
## 222 Not Awarded  0.000000000     Awarded  1.014925373 Not Awarded  0.000000000
## 223     Awarded  0.119402985     Awarded  0.179104478     Awarded  0.194029851
## 224     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 225 Not Awarded  0.000000000     Awarded  0.164179104     Awarded  0.164179104
## 226 Not Awarded  0.000000000     Awarded  0.104477612 Not Awarded  0.000000000
## 227 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 228     Awarded  0.012820513 Not Awarded  0.000000000 Not Awarded  0.000000000
## 229     Awarded  0.435897436     Awarded  0.179487179     Awarded  0.435897436
## 230 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 231     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 232 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 233     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 234     Awarded  0.128205128     Awarded  0.384615385     Awarded  0.384615385
## 235 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 236 Not Awarded  0.000000000     Awarded  1.200000000 Not Awarded  0.000000000
## 237 Not Awarded  0.000000000     Awarded  1.000000000     Awarded  1.000000000
## 238 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 239     Awarded  0.400000000     Awarded  0.800000000     Awarded  0.800000000
## 240     Awarded  1.200000000     Awarded  1.400000000     Awarded  1.400000000
## 241 Not Awarded  0.000000000     Awarded  1.021739130 Not Awarded  0.000000000
## 242 Not Awarded  0.000000000     Awarded  1.021739130 Not Awarded  0.000000000
## 243     Awarded  0.173913043     Awarded  0.413043478     Awarded  0.413043478
## 244     Awarded  0.521739130     Awarded  0.760869565     Awarded  0.782608696
## 245 Not Awarded  0.000000000     Awarded  1.021739130 Not Awarded  0.000000000
## 246 Not Awarded  0.000000000     Awarded  1.021739130 Not Awarded  0.000000000
## 247     Awarded  0.521739130     Awarded  0.565217391     Awarded  0.565217391
## 248     Awarded  1.021739130     Awarded  1.021739130     Awarded  1.021739130
## 249     Awarded  0.015151515 Not Awarded  0.000000000     Awarded  0.015151515
## 250     Awarded  3.136363636     Awarded  1.166666667     Awarded  3.136363636
## 251     Awarded  0.242424242     Awarded  0.242424242     Awarded  0.242424242
## 252 Not Awarded  0.000000000     Awarded  0.090909091     Awarded  0.090909091
## 253 Not Awarded  0.000000000     Awarded  0.121212121     Awarded  0.121212121
## 254 Not Awarded  0.000000000     Awarded  0.212121212     Awarded  0.212121212
## 255 Not Awarded  0.000000000     Awarded  0.196969697     Awarded  0.196969697
## 256 Not Awarded  0.000000000     Awarded  0.409090909     Awarded  0.409090909
## 257 Not Awarded  0.000000000     Awarded  0.015151515 Not Awarded  0.000000000
## 258     Awarded  0.242424242     Awarded  0.924242424     Awarded  0.939393939
## 259     Awarded  0.010526316     Awarded  0.010526316     Awarded  0.010526316
## 260     Awarded  0.031578947     Awarded  0.063157895     Awarded  0.063157895
## 261     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 262 Not Awarded  0.000000000     Awarded  0.473684211     Awarded  0.473684211
## 263     Awarded  0.105263158 Not Awarded  0.000000000     Awarded  0.105263158
## 264     Awarded  0.042105263     Awarded  0.052631579     Awarded  0.052631579
## 265 Not Awarded  0.000000000     Awarded  0.242105263     Awarded  0.242105263
## 266     Awarded  0.507874016     Awarded  1.000000000     Awarded  1.000000000
## 267     Awarded  0.185039370     Awarded  0.433070866     Awarded  0.444881890
## 268 Not Awarded  0.000000000     Awarded  0.015748031     Awarded  0.015748031
## 269     Awarded  0.062992126     Awarded  0.102362205     Awarded  0.106299213
## 270     Awarded  0.094488189     Awarded  0.173228346     Awarded  0.169291339
## 271 Not Awarded  0.000000000     Awarded  0.141732283     Awarded  0.141732283
## 272 Not Awarded  0.000000000     Awarded  0.019685039     Awarded  0.019685039
## 273 Not Awarded  0.000000000     Awarded  0.125984252 Not Awarded  0.000000000
## 274     Awarded  0.051181102     Awarded  0.082677165     Awarded  0.082677165
## 275     Awarded  0.161417323     Awarded  0.208661417     Awarded  0.208661417
## 276 Not Awarded  0.000000000     Awarded  0.003937008     Awarded  0.003937008
## 277     Awarded  0.011811024 Not Awarded  0.000000000     Awarded  0.011811024
## 278     Awarded  0.062992126     Awarded  0.066929134     Awarded  0.082677165
## 279 Not Awarded  0.000000000     Awarded  0.200787402     Awarded  0.200787402
## 280     Awarded  1.011811024     Awarded  1.011811024     Awarded  1.011811024
## 281     Awarded  0.003937008     Awarded  0.003937008 Not Awarded  0.000000000
## 282     Awarded  1.241379310     Awarded  1.241379310     Awarded  1.241379310
## 283     Awarded  0.051851852     Awarded  0.066666667     Awarded  0.074074074
## 284 Not Awarded  0.000000000     Awarded  0.237037037     Awarded  0.237037037
## 285     Awarded  0.007407407     Awarded  0.214814815     Awarded  0.214814815
## 286     Awarded  1.022222222     Awarded  1.022222222     Awarded  1.022222222
## 287 Not Awarded  0.000000000     Awarded  0.511111111     Awarded  0.503703704
## 288 Not Awarded  0.000000000     Awarded  0.992592593 Not Awarded  0.000000000
## 289 Not Awarded  0.000000000     Awarded  0.714285714     Awarded  0.714285714
## 290 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 291     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 292 Not Awarded  0.000000000     Awarded  0.428571429     Awarded  0.428571429
## 293 Not Awarded  0.000000000     Awarded  0.461538462     Awarded  0.076923077
## 294 Not Awarded  0.000000000     Awarded  0.384615385     Awarded  0.384615385
## 295     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.717948718
## 296 Not Awarded  0.000000000     Awarded  0.410256410     Awarded  0.410256410
## 297 Not Awarded  0.000000000     Awarded  0.230769231     Awarded  0.230769231
## 298 Not Awarded  0.000000000     Awarded  0.282051282     Awarded  0.282051282
## 299 Not Awarded  0.000000000     Awarded  0.076923077 Not Awarded  0.000000000
## 300 Not Awarded  0.000000000     Awarded  0.384615385 Not Awarded  0.000000000
## 301 Not Awarded  0.000000000     Awarded  0.025641026 Not Awarded  0.000000000
## 302 Not Awarded  0.000000000     Awarded  0.076923077     Awarded  0.076923077
## 303     Awarded  0.102564103     Awarded  0.102564103     Awarded  0.102564103
## 304 Not Awarded  0.000000000     Awarded  0.055555556     Awarded  0.055555556
## 305     Awarded  0.125000000     Awarded  0.194444444     Awarded  0.194444444
## 306 Not Awarded  0.000000000     Awarded  0.138888889     Awarded  0.138888889
## 307     Awarded  1.166666667     Awarded  1.152777778     Awarded  1.166666667
## 308 Not Awarded  0.000000000     Awarded  0.111111111     Awarded  0.111111111
## 309 Not Awarded  0.000000000     Awarded  0.277777778     Awarded  0.277777778
## 310 Not Awarded  0.000000000     Awarded  0.083333333     Awarded  0.083333333
## 311 Not Awarded  0.000000000     Awarded  0.125000000     Awarded  0.125000000
## 312 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 313     Awarded  0.218181818     Awarded  0.327272727     Awarded  0.327272727
## 314 Not Awarded  0.000000000     Awarded  0.145454545     Awarded  0.145454545
## 315 Not Awarded  0.000000000     Awarded  0.381818182     Awarded  0.381818182
## 316 Not Awarded  0.000000000     Awarded  0.327272727     Awarded  0.327272727
## 317     Awarded  0.054545455     Awarded  0.054545455     Awarded  0.054545455
## 318     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 319     Awarded  0.363636364     Awarded  0.854545455     Awarded  0.854545455
## 320     Awarded  0.109090909     Awarded  0.200000000     Awarded  0.200000000
## 321 Not Awarded  0.000000000     Awarded  0.581818182     Awarded  0.581818182
## 322     Awarded  1.043478261     Awarded  1.043478261     Awarded  1.043478261
## 323     Awarded  0.086956522     Awarded  0.173913043     Awarded  0.173913043
## 324     Awarded  0.117647059     Awarded  0.147058824     Awarded  0.147058824
## 325     Awarded  2.470588235     Awarded  2.470588235     Awarded  1.000000000
## 326     Awarded  0.088235294     Awarded  0.088235294     Awarded  0.088235294
## 327 Not Awarded  0.000000000     Awarded  0.147058824     Awarded  0.147058824
## 328     Awarded  1.029850746     Awarded  1.029850746     Awarded  1.029850746
## 329     Awarded  0.044776119     Awarded  0.059701493     Awarded  0.059701493
## 330     Awarded  0.044776119     Awarded  0.223880597     Awarded  0.223880597
## 331     Awarded  0.134328358     Awarded  0.313432836     Awarded  0.313432836
## 332     Awarded  0.120000000     Awarded  0.373333333     Awarded  0.386666667
## 333     Awarded  0.053333333     Awarded  0.146666667     Awarded  0.160000000
## 334     Awarded  0.213333333     Awarded  0.400000000     Awarded  0.426666667
## 335     Awarded  0.146666667     Awarded  0.426666667     Awarded  0.506666667
## 336 Not Awarded  0.000000000     Awarded  0.186666667     Awarded  0.186666667
## 337     Awarded  1.013333333     Awarded  1.013333333     Awarded  1.013333333
## 338     Awarded  0.173333333     Awarded  0.226666667     Awarded  0.213333333
## 339 Not Awarded  0.000000000     Awarded  0.160000000     Awarded  0.160000000
## 340 Not Awarded  0.000000000     Awarded  2.400000000 Not Awarded  0.000000000
## 341     Awarded  2.400000000     Awarded  2.400000000     Awarded  2.400000000
## 342     Awarded  0.066666667     Awarded  0.066666667     Awarded  0.066666667
## 343     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 344 Not Awarded  0.000000000     Awarded  0.086206897 Not Awarded  0.000000000
## 345 Not Awarded  0.000000000     Awarded  0.051724138 Not Awarded  0.000000000
## 346 Not Awarded  0.000000000     Awarded  0.189655172     Awarded  0.189655172
## 347 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 348     Awarded  0.172413793     Awarded  0.206896552     Awarded  0.206896552
## 349 Not Awarded  0.000000000     Awarded  0.034482759 Not Awarded  0.000000000
## 350     Awarded  0.034482759     Awarded  0.034482759     Awarded  0.034482759
## 351     Awarded  0.068965517     Awarded  0.034482759     Awarded  0.068965517
## 352     Awarded  0.258620690     Awarded  0.344827586     Awarded  0.327586207
## 353     Awarded  0.051724138     Awarded  0.051724138     Awarded  0.051724138
## 354 Not Awarded  0.000000000     Awarded  0.017241379     Awarded  0.017241379
## 355 Not Awarded  0.000000000     Awarded  0.017241379 Not Awarded  0.000000000
## 356 Not Awarded  0.000000000     Awarded  0.586206897     Awarded  0.586206897
## 357 Not Awarded  0.000000000     Awarded  0.051724138 Not Awarded  0.000000000
## 358     Awarded  0.034482759     Awarded  0.034482759     Awarded  0.034482759
## 359     Awarded  0.137931034     Awarded  0.155172414     Awarded  0.155172414
## 360     Awarded  0.103448276     Awarded  0.103448276     Awarded  0.103448276
## 361 Not Awarded  0.000000000     Awarded  0.758620690     Awarded  0.758620690
## 362 Not Awarded  0.000000000     Awarded  0.234375000     Awarded  0.234375000
## 363     Awarded  0.031250000     Awarded  0.046875000     Awarded  0.046875000
## 364     Awarded  1.031250000     Awarded  1.031250000     Awarded  1.031250000
## 365 Not Awarded  0.000000000     Awarded  0.234375000 Not Awarded  0.000000000
## 366     Awarded  0.171875000     Awarded  0.281250000     Awarded  0.281250000
## 367 Not Awarded  0.000000000     Awarded  1.250000000 Not Awarded  0.000000000
## 368     Awarded  1.500000000     Awarded  1.500000000     Awarded  1.500000000
## 369     Awarded  0.625000000     Awarded  1.000000000     Awarded  1.000000000
## 370 Not Awarded  0.000000000     Awarded  1.250000000     Awarded  1.250000000
## 371 Not Awarded  0.000000000     Awarded  1.125000000     Awarded  1.125000000
## 372 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 373     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 374 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 375     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 376 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 377     Awarded  0.014925373     Awarded  0.014925373 Not Awarded  0.000000000
## 378 Not Awarded  0.000000000     Awarded  0.417910448 Not Awarded  0.000000000
## 379 Not Awarded  0.000000000     Awarded  0.522388060 Not Awarded  0.000000000
## 380     Awarded  0.074626866     Awarded  0.134328358     Awarded  0.134328358
## 381     Awarded  0.134328358     Awarded  0.268656716     Awarded  0.268656716
## 382     Awarded  0.074626866     Awarded  0.208955224     Awarded  0.208955224
## 383     Awarded  0.328358209     Awarded  0.447761194     Awarded  0.507462687
## 384     Awarded  0.731343284     Awarded  1.000000000     Awarded  1.000000000
## 385     Awarded  0.089552239     Awarded  0.089552239     Awarded  0.089552239
## 386     Awarded  1.044776119     Awarded  1.044776119     Awarded  1.044776119
## 387     Awarded  0.179104478     Awarded  0.268656716     Awarded  0.238805970
## 388 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 389 Not Awarded  0.000000000     Awarded  0.075471698 Not Awarded  0.000000000
## 390     Awarded  0.006289308     Awarded  0.044025157     Awarded  0.044025157
## 391     Awarded  1.006289308     Awarded  1.006289308     Awarded  1.006289308
## 392     Awarded  0.062893082     Awarded  0.125786164     Awarded  0.125786164
## 393     Awarded  0.050314465     Awarded  0.144654088     Awarded  0.144654088
## 394     Awarded  0.044025157     Awarded  1.000000000     Awarded  1.000000000
## 395     Awarded  0.125786164     Awarded  0.433962264     Awarded  0.433962264
## 396 Not Awarded  0.000000000     Awarded  0.188679245 Not Awarded  0.000000000
## 397 Not Awarded  0.000000000     Awarded  0.800000000 Not Awarded  0.000000000
## 398     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 399     Awarded  0.200000000     Awarded  0.200000000     Awarded  0.200000000
## 400     Awarded  0.400000000     Awarded  0.400000000     Awarded  0.400000000
## 401 Not Awarded  0.000000000     Awarded  0.800000000 Not Awarded  0.000000000
## 402     Awarded  0.121212121     Awarded  0.232323232     Awarded  0.232323232
## 403 Not Awarded  0.000000000     Awarded  0.494949495     Awarded  0.494949495
## 404 Not Awarded  0.000000000     Awarded  0.202020202     Awarded  0.202020202
## 405     Awarded  0.101010101     Awarded  0.808080808     Awarded  0.808080808
## 406     Awarded  1.020202020     Awarded  1.020202020     Awarded  1.020202020
## 407 Not Awarded  0.000000000     Awarded  0.204545455     Awarded  0.204545455
## 408 Not Awarded  0.000000000     Awarded  0.113636364     Awarded  0.113636364
## 409     Awarded  1.113636364     Awarded  1.000000000     Awarded  1.000000000
## 410 Not Awarded  0.000000000     Awarded  0.250000000     Awarded  0.250000000
## 411     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 412     Awarded  0.147058824 Not Awarded  0.000000000     Awarded  0.147058824
## 413 Not Awarded  0.000000000     Awarded  0.274509804     Awarded  0.274509804
## 414     Awarded  0.343137255     Awarded  0.392156863     Awarded  0.460784314
## 415     Awarded  0.065217391     Awarded  0.054347826     Awarded  0.065217391
## 416     Awarded  0.239130435     Awarded  0.304347826     Awarded  0.336956522
## 417     Awarded  1.010869565     Awarded  1.010869565     Awarded  1.010869565
## 418     Awarded  1.152380952     Awarded  1.152380952     Awarded  1.152380952
## 419 Not Awarded  0.000000000     Awarded  0.314285714 Not Awarded  0.000000000
## 420 Not Awarded  0.000000000     Awarded  0.133333333     Awarded  0.133333333
## 421 Not Awarded  0.000000000     Awarded  0.491666667     Awarded  0.491666667
## 422     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 423     Awarded  0.175000000     Awarded  0.133333333     Awarded  0.191666667
## 424 Not Awarded  0.000000000     Awarded  0.183333333     Awarded  0.183333333
## 425 Not Awarded  0.000000000     Awarded  0.508333333     Awarded  0.508333333
## 426     Awarded  0.187500000     Awarded  1.000000000     Awarded  1.000000000
## 427 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 428 Not Awarded  0.000000000     Awarded  0.640625000 Not Awarded  0.000000000
## 429     Awarded  0.343750000     Awarded  0.406250000     Awarded  0.406250000
## 430 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 431     Awarded  0.406250000     Awarded  0.859375000     Awarded  0.859375000
## 432     Awarded  0.390625000     Awarded  1.000000000     Awarded  1.000000000
## 433 Not Awarded  0.000000000     Awarded  0.296875000 Not Awarded  0.000000000
## 434     Awarded  0.328125000     Awarded  1.000000000     Awarded  1.000000000
## 435 Not Awarded  0.000000000     Awarded  0.531250000 Not Awarded  0.000000000
## 436     Awarded  0.093750000     Awarded  0.546875000     Awarded  0.546875000
## 437     Awarded  0.562500000     Awarded  0.578125000     Awarded  0.578125000
## 438 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 439 Not Awarded  0.000000000     Awarded  0.187500000 Not Awarded  0.000000000
## 440     Awarded  1.109375000     Awarded  1.109375000     Awarded  1.109375000
## 441 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 442 Not Awarded  0.000000000     Awarded  0.285714286 Not Awarded  0.000000000
## 443 Not Awarded  0.000000000     Awarded  0.428571429     Awarded  0.428571429
## 444 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 445 Not Awarded  0.000000000     Awarded  0.714285714     Awarded  0.714285714
## 446     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.214285714
## 447 Not Awarded  0.000000000     Awarded  0.357142857     Awarded  0.357142857
## 448     Awarded 27.687500000     Awarded 27.312500000     Awarded 27.250000000
## 449 Not Awarded  0.000000000     Awarded  0.812500000     Awarded  0.812500000
## 450     Awarded  0.060240964     Awarded  0.060240964     Awarded  0.060240964
## 451     Awarded  1.132530120     Awarded  1.132530120     Awarded  1.132530120
## 452     Awarded  0.048192771 Not Awarded  0.000000000     Awarded  0.048192771
## 453 Not Awarded  0.000000000     Awarded  0.192771084     Awarded  0.192771084
## 454     Awarded  0.036144578     Awarded  0.036144578     Awarded  0.036144578
## 455     Awarded  0.048192771 Not Awarded  0.000000000     Awarded  0.048192771
## 456 Not Awarded  0.000000000     Awarded  1.206896552 Not Awarded  0.000000000
## 457 Not Awarded  0.000000000     Awarded  0.632183908     Awarded  0.632183908
## 458 Not Awarded  0.000000000     Awarded  0.206896552     Awarded  0.206896552
## 459     Awarded  0.080459770     Awarded  0.103448276     Awarded  0.114942529
## 460 Not Awarded  0.000000000     Awarded  0.057471264     Awarded  0.057471264
## 461 Not Awarded  0.000000000     Awarded  0.011494253     Awarded  0.011494253
## 462 Not Awarded  0.000000000     Awarded  0.206896552     Awarded  0.206896552
## 463     Awarded  1.229885057     Awarded  1.229885057     Awarded  1.229885057
## 464 Not Awarded  0.000000000     Awarded  0.356321839     Awarded  0.356321839
## 465 Not Awarded  0.000000000     Awarded  0.459770115     Awarded  0.459770115
## 466     Awarded  0.304347826     Awarded  0.460869565     Awarded  0.478260870
## 467     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 468 Not Awarded  0.000000000     Awarded  0.643478261 Not Awarded  0.000000000
## 469 Not Awarded  0.000000000     Awarded  0.660869565     Awarded  0.660869565
## 470     Awarded  0.286956522     Awarded  0.365217391     Awarded  0.452173913
## 471 Not Awarded  0.000000000     Awarded  0.139130435     Awarded  0.139130435
## 472     Awarded  0.226086957     Awarded  0.721739130     Awarded  0.756521739
## 473     Awarded  0.146341463     Awarded  0.121951220     Awarded  0.158536585
## 474     Awarded  0.268292683     Awarded  0.597560976     Awarded  0.597560976
## 475     Awarded  0.109756098     Awarded  0.341463415     Awarded  0.353658537
## 476 Not Awarded  0.000000000     Awarded  1.012195122 Not Awarded  0.000000000
## 477     Awarded  0.048780488     Awarded  0.036585366     Awarded  0.048780488
## 478     Awarded  0.207317073     Awarded  0.341463415     Awarded  0.353658537
## 479     Awarded  1.012195122     Awarded  1.012195122     Awarded  1.012195122
## 480     Awarded  0.097560976     Awarded  0.146341463     Awarded  0.134146341
## 481 Not Awarded  0.000000000     Awarded  0.267857143     Awarded  0.267857143
## 482     Awarded  1.142857143     Awarded  1.142857143     Awarded  1.142857143
## 483 Not Awarded  0.000000000     Awarded  0.190000000     Awarded  0.190000000
## 484 Not Awarded  0.000000000     Awarded  0.660000000 Not Awarded  0.000000000
## 485 Not Awarded  0.000000000     Awarded  1.010000000 Not Awarded  0.000000000
## 486 Not Awarded  0.000000000     Awarded  0.280000000     Awarded  0.260000000
## 487     Awarded  0.340000000     Awarded  0.510000000     Awarded  0.520000000
## 488     Awarded  1.060000000     Awarded  1.060000000     Awarded  1.060000000
## 489     Awarded  0.450000000     Awarded  0.500000000     Awarded  0.500000000
## 490 Not Awarded  0.000000000     Awarded  0.140000000     Awarded  0.140000000
## 491 Not Awarded  0.000000000     Awarded  0.210000000     Awarded  0.210000000
## 492 Not Awarded  0.000000000     Awarded  0.188679245     Awarded  0.188679245
## 493 Not Awarded  0.000000000     Awarded  0.207547170     Awarded  0.207547170
## 494 Not Awarded  0.000000000     Awarded  0.113207547 Not Awarded  0.000000000
## 495     Awarded  1.094339623     Awarded  1.094339623     Awarded  1.094339623
## 496 Not Awarded  0.000000000     Awarded  0.021505376     Awarded  0.021505376
## 497 Not Awarded  0.000000000     Awarded  0.129032258     Awarded  0.129032258
## 498     Awarded  1.086021505     Awarded  1.086021505     Awarded  1.086021505
## 499 Not Awarded  0.000000000     Awarded  0.301075269     Awarded  0.301075269
## 500     Awarded  0.322580645     Awarded  0.956989247     Awarded  0.956989247
## 501 Not Awarded  0.000000000     Awarded  0.100000000     Awarded  0.100000000
## 502 Not Awarded  0.000000000     Awarded  0.800000000     Awarded  0.800000000
## 503 Not Awarded  0.000000000     Awarded  0.600000000     Awarded  0.600000000
## 504     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 505     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 506     Awarded  1.095238095     Awarded  1.095238095     Awarded  1.095238095
## 507     Awarded  0.571428571     Awarded  1.000000000     Awarded  1.000000000
## 508 Not Awarded  0.000000000     Awarded  0.030303030     Awarded  0.030303030
## 509 Not Awarded  0.000000000     Awarded  0.030303030     Awarded  0.030303030
## 510     Awarded  2.090909091     Awarded  2.090909091     Awarded  2.090909091
## 511     Awarded  2.588235294     Awarded  2.588235294     Awarded  2.588235294
## 512 Not Awarded  0.000000000     Awarded  0.411764706     Awarded  0.411764706
## 513 Not Awarded  0.000000000     Awarded  0.470588235     Awarded  0.470588235
## 514 Not Awarded  0.000000000     Awarded  0.419354839 Not Awarded  0.000000000
## 515     Awarded  1.032258065     Awarded  1.032258065     Awarded  1.032258065
## 516     Awarded  0.209677419     Awarded  0.225806452     Awarded  0.225806452
## 517 Not Awarded  0.000000000     Awarded  0.258064516     Awarded  0.258064516
## 518     Awarded  0.145161290     Awarded  0.225806452     Awarded  0.225806452
## 519 Not Awarded  0.000000000     Awarded  0.177419355     Awarded  0.177419355
## 520     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 521 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 522 Not Awarded  0.000000000     Awarded  0.443181818     Awarded  0.443181818
## 523     Awarded  0.125000000     Awarded  0.045454545     Awarded  0.136363636
## 524 Not Awarded  0.000000000     Awarded  0.250000000     Awarded  0.250000000
## 525 Not Awarded  0.000000000     Awarded  0.022727273     Awarded  0.022727273
## 526     Awarded  0.116883117     Awarded  0.259740260     Awarded  0.272727273
## 527 Not Awarded  0.000000000     Awarded  0.012987013 Not Awarded  0.000000000
## 528     Awarded  0.584415584     Awarded  0.766233766     Awarded  0.818181818
## 529 Not Awarded  0.000000000     Awarded  0.012987013 Not Awarded  0.000000000
## 530 Not Awarded  0.000000000     Awarded  0.207792208     Awarded  0.207792208
## 531 Not Awarded  0.000000000     Awarded  0.012987013 Not Awarded  0.000000000
## 532 Not Awarded  0.000000000     Awarded  0.272727273     Awarded  0.272727273
## 533     Awarded  1.454545455     Awarded  1.454545455     Awarded  1.454545455
## 534     Awarded  0.025974026 Not Awarded  0.000000000     Awarded  0.025974026
## 535     Awarded  0.350649351     Awarded  0.662337662     Awarded  0.662337662
## 536 Not Awarded  0.000000000     Awarded  0.138888889     Awarded  0.138888889
## 537 Not Awarded  0.000000000     Awarded  0.166666667     Awarded  0.166666667
## 538 Not Awarded  0.000000000     Awarded  0.055555556     Awarded  0.055555556
## 539 Not Awarded  0.000000000     Awarded  0.111111111     Awarded  0.111111111
## 540     Awarded  0.222222222     Awarded  0.555555556     Awarded  0.555555556
## 541 Not Awarded  0.000000000     Awarded  0.333333333     Awarded  0.333333333
## 542     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.305555556
## 543 Not Awarded  0.000000000     Awarded  0.388888889     Awarded  0.388888889
## 544 Not Awarded  0.000000000     Awarded  0.164179104     Awarded  0.164179104
## 545 Not Awarded  0.000000000     Awarded  1.014925373 Not Awarded  0.000000000
## 546     Awarded  0.119402985     Awarded  0.179104478     Awarded  0.194029851
## 547     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 548 Not Awarded  0.000000000     Awarded  0.164179104     Awarded  0.164179104
## 549 Not Awarded  0.000000000     Awarded  0.104477612 Not Awarded  0.000000000
## 550 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 551     Awarded  0.012820513 Not Awarded  0.000000000 Not Awarded  0.000000000
## 552     Awarded  0.435897436     Awarded  0.179487179     Awarded  0.435897436
## 553 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 554     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 555 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 556     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 557     Awarded  0.128205128     Awarded  0.384615385     Awarded  0.384615385
## 558 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 559 Not Awarded  0.000000000     Awarded  1.200000000 Not Awarded  0.000000000
## 560 Not Awarded  0.000000000     Awarded  1.000000000     Awarded  1.000000000
## 561 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 562     Awarded  0.400000000     Awarded  0.800000000     Awarded  0.800000000
## 563     Awarded  1.200000000     Awarded  1.400000000     Awarded  1.400000000
## 564 Not Awarded  0.000000000     Awarded  1.021739130 Not Awarded  0.000000000
## 565 Not Awarded  0.000000000     Awarded  1.021739130 Not Awarded  0.000000000
## 566     Awarded  0.173913043     Awarded  0.413043478     Awarded  0.413043478
## 567     Awarded  0.521739130     Awarded  0.760869565     Awarded  0.782608696
## 568 Not Awarded  0.000000000     Awarded  1.021739130 Not Awarded  0.000000000
## 569 Not Awarded  0.000000000     Awarded  1.021739130 Not Awarded  0.000000000
## 570     Awarded  0.521739130     Awarded  0.565217391     Awarded  0.565217391
## 571     Awarded  1.021739130     Awarded  1.021739130     Awarded  1.021739130
## 572     Awarded  0.015151515 Not Awarded  0.000000000     Awarded  0.015151515
## 573     Awarded  3.136363636     Awarded  1.166666667     Awarded  3.136363636
## 574     Awarded  0.242424242     Awarded  0.242424242     Awarded  0.242424242
## 575 Not Awarded  0.000000000     Awarded  0.090909091     Awarded  0.090909091
## 576 Not Awarded  0.000000000     Awarded  0.121212121     Awarded  0.121212121
## 577 Not Awarded  0.000000000     Awarded  0.212121212     Awarded  0.212121212
## 578 Not Awarded  0.000000000     Awarded  0.196969697     Awarded  0.196969697
## 579 Not Awarded  0.000000000     Awarded  0.409090909     Awarded  0.409090909
## 580 Not Awarded  0.000000000     Awarded  0.015151515 Not Awarded  0.000000000
## 581     Awarded  0.242424242     Awarded  0.924242424     Awarded  0.939393939
## 582     Awarded  0.010526316     Awarded  0.010526316     Awarded  0.010526316
## 583     Awarded  0.031578947     Awarded  0.063157895     Awarded  0.063157895
## 584     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 585 Not Awarded  0.000000000     Awarded  0.473684211     Awarded  0.473684211
## 586     Awarded  0.105263158 Not Awarded  0.000000000     Awarded  0.105263158
## 587     Awarded  0.042105263     Awarded  0.052631579     Awarded  0.052631579
## 588 Not Awarded  0.000000000     Awarded  0.242105263     Awarded  0.242105263
## 589     Awarded  0.507874016     Awarded  1.000000000     Awarded  1.000000000
## 590     Awarded  0.185039370     Awarded  0.433070866     Awarded  0.444881890
## 591 Not Awarded  0.000000000     Awarded  0.015748031     Awarded  0.015748031
## 592     Awarded  0.062992126     Awarded  0.102362205     Awarded  0.106299213
## 593     Awarded  0.094488189     Awarded  0.173228346     Awarded  0.169291339
## 594 Not Awarded  0.000000000     Awarded  0.141732283     Awarded  0.141732283
## 595 Not Awarded  0.000000000     Awarded  0.019685039     Awarded  0.019685039
## 596 Not Awarded  0.000000000     Awarded  0.125984252 Not Awarded  0.000000000
## 597     Awarded  0.051181102     Awarded  0.082677165     Awarded  0.082677165
## 598     Awarded  0.161417323     Awarded  0.208661417     Awarded  0.208661417
## 599 Not Awarded  0.000000000     Awarded  0.003937008     Awarded  0.003937008
## 600     Awarded  0.011811024 Not Awarded  0.000000000     Awarded  0.011811024
## 601     Awarded  0.062992126     Awarded  0.066929134     Awarded  0.082677165
## 602 Not Awarded  0.000000000     Awarded  0.200787402     Awarded  0.200787402
## 603     Awarded  1.011811024     Awarded  1.011811024     Awarded  1.011811024
## 604     Awarded  0.003937008     Awarded  0.003937008 Not Awarded  0.000000000
## 605     Awarded  1.241379310     Awarded  1.241379310     Awarded  1.241379310
## 606     Awarded  0.051851852     Awarded  0.066666667     Awarded  0.074074074
## 607 Not Awarded  0.000000000     Awarded  0.237037037     Awarded  0.237037037
## 608     Awarded  0.007407407     Awarded  0.214814815     Awarded  0.214814815
## 609     Awarded  1.022222222     Awarded  1.022222222     Awarded  1.022222222
## 610 Not Awarded  0.000000000     Awarded  0.511111111     Awarded  0.503703704
## 611 Not Awarded  0.000000000     Awarded  0.992592593 Not Awarded  0.000000000
## 612 Not Awarded  0.000000000     Awarded  0.714285714     Awarded  0.714285714
## 613 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 614     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 615 Not Awarded  0.000000000     Awarded  0.428571429     Awarded  0.428571429
## 616 Not Awarded  0.000000000     Awarded  0.461538462     Awarded  0.076923077
## 617 Not Awarded  0.000000000     Awarded  0.384615385     Awarded  0.384615385
## 618     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.717948718
## 619 Not Awarded  0.000000000     Awarded  0.410256410     Awarded  0.410256410
## 620 Not Awarded  0.000000000     Awarded  0.230769231     Awarded  0.230769231
## 621 Not Awarded  0.000000000     Awarded  0.282051282     Awarded  0.282051282
## 622 Not Awarded  0.000000000     Awarded  0.076923077 Not Awarded  0.000000000
## 623 Not Awarded  0.000000000     Awarded  0.384615385 Not Awarded  0.000000000
## 624 Not Awarded  0.000000000     Awarded  0.025641026 Not Awarded  0.000000000
## 625 Not Awarded  0.000000000     Awarded  0.076923077     Awarded  0.076923077
## 626     Awarded  0.102564103     Awarded  0.102564103     Awarded  0.102564103
## 627 Not Awarded  0.000000000     Awarded  0.055555556     Awarded  0.055555556
## 628     Awarded  0.125000000     Awarded  0.194444444     Awarded  0.194444444
## 629 Not Awarded  0.000000000     Awarded  0.138888889     Awarded  0.138888889
## 630     Awarded  1.166666667     Awarded  1.152777778     Awarded  1.166666667
## 631 Not Awarded  0.000000000     Awarded  0.111111111     Awarded  0.111111111
## 632 Not Awarded  0.000000000     Awarded  0.277777778     Awarded  0.277777778
## 633 Not Awarded  0.000000000     Awarded  0.083333333     Awarded  0.083333333
## 634 Not Awarded  0.000000000     Awarded  0.125000000     Awarded  0.125000000
## 635 Not Awarded  0.000000000     Awarded  1.000000000 Not Awarded  0.000000000
## 636     Awarded  0.218181818     Awarded  0.327272727     Awarded  0.327272727
## 637 Not Awarded  0.000000000     Awarded  0.145454545     Awarded  0.145454545
## 638 Not Awarded  0.000000000     Awarded  0.381818182     Awarded  0.381818182
## 639 Not Awarded  0.000000000     Awarded  0.327272727     Awarded  0.327272727
## 640     Awarded  0.054545455     Awarded  0.054545455     Awarded  0.054545455
## 641     Awarded  1.000000000     Awarded  1.000000000     Awarded  1.000000000
## 642     Awarded  0.363636364     Awarded  0.854545455     Awarded  0.854545455
## 643     Awarded  0.109090909     Awarded  0.200000000     Awarded  0.200000000
## 644 Not Awarded  0.000000000     Awarded  0.581818182     Awarded  0.581818182
## 645     Awarded  1.043478261     Awarded  1.043478261     Awarded  1.043478261
## 646     Awarded  0.086956522     Awarded  0.173913043     Awarded  0.173913043
##     TotalAgencies FEMACSAvg FundType          funds
## 1              10 0.9766411   ReqAmt    2434844.857
## 2               8 0.7550000   ReqAmt   19863078.852
## 3               6 1.0000000   ReqAmt    5197162.960
## 4               1 1.0000000   ReqAmt       1846.870
## 5              10 0.9551913   ReqAmt   35717008.133
## 6               2 1.0000000   ReqAmt     201660.260
## 7               4 1.0000000   ReqAmt     957840.990
## 8               2 1.0000000   ReqAmt     113969.080
## 9               2 1.0000000   ReqAmt     637446.550
## 10              5 1.0000000   ReqAmt     308420.770
## 11              2 1.0000000   ReqAmt      16978.890
## 12              4 1.0000000   ReqAmt     639155.790
## 13              1 1.0000000   ReqAmt     113021.520
## 14              5 0.9391026   ReqAmt    8963656.662
## 15              3 1.0000000   ReqAmt    1207244.700
## 16              3 1.0000000   ReqAmt      25315.020
## 17              8 0.8936012   ReqAmt    3529626.690
## 18              9 0.9198390   ReqAmt  162666766.716
## 19              4 0.9062500   ReqAmt     644115.000
## 20             17 0.9559613   ReqAmt  412969248.115
## 21             13 0.9871795   ReqAmt      73939.550
## 22              9 1.0000000   ReqAmt     111265.000
## 23              1 1.0000000   ReqAmt     315991.420
## 24             14 0.9341270   ReqAmt      38242.252
## 25              8 0.9285714   ReqAmt    6053446.853
## 26              1 1.0000000   ReqAmt      64018.400
## 27             16 1.0000000   ReqAmt     210464.360
## 28              8 0.9816176   ReqAmt   58462371.498
## 29             17 0.9658304   ReqAmt   28294846.186
## 30             19 0.9164789   ReqAmt   53400370.670
## 31              8 0.9513889   ReqAmt    8672055.556
## 32             10 1.0000000   ReqAmt      42664.260
## 33              2 1.0000000   ReqAmt     216446.440
## 34              9 1.0000000   ReqAmt       9933.690
## 35              9 0.9715414   ReqAmt    4132990.190
## 36             17 0.9628422   ReqAmt  670111993.343
## 37             12 0.9599868   ReqAmt    7207299.594
## 38              2 1.0000000   ReqAmt    1721710.130
## 39              3 1.0000000   ReqAmt     221173.140
## 40              4 1.0000000   ReqAmt       3653.860
## 41             13 0.9331854   ReqAmt   80554165.268
## 42             11 0.9738636   ReqAmt     555593.710
## 43             19 0.9717086   ReqAmt    8533396.606
## 44              1 0.7500000   ReqAmt    1130890.200
## 45              3 0.8272198   ReqAmt   33230911.308
## 46             15 0.9916667   ReqAmt    1280811.180
## 47              2 1.0000000   ReqAmt     132717.390
## 48              2 1.0000000   ReqAmt      15267.200
## 49             15 0.9958333   ReqAmt    1306839.321
## 50              4 0.7500000   ReqAmt   24163037.250
## 51             15 1.0000000   ReqAmt    1388233.930
## 52              9 0.9537701   ReqAmt   29138452.633
## 53              5 0.8333333   ReqAmt     810984.676
## 54              3 0.9444444   ReqAmt     241737.035
## 55              7 0.9553571   ReqAmt    2232723.088
## 56              3 1.0000000   ReqAmt       2666.200
## 57              1 1.0000000   ReqAmt       5402.120
## 58              4 0.8750000   ReqAmt    1374983.335
## 59              5 0.9500000   ReqAmt     107678.230
## 60              4 1.0000000   ReqAmt      15134.030
## 61             25 0.9681893   ReqAmt  206435753.579
## 62              2 1.0000000   ReqAmt      24006.000
## 63              6 0.9045940   ReqAmt  212878073.490
## 64             22 0.9575652   ReqAmt   65488854.663
## 65             17 0.9440368   ReqAmt    2451923.077
## 66              1 1.0000000   ReqAmt      15517.410
## 67              2 1.0000000   ReqAmt     105956.560
## 68              3 0.8622449   ReqAmt  125561025.953
## 69              8 1.0000000   ReqAmt     953479.950
## 70              1 1.0000000   ReqAmt     297671.400
## 71             22 0.9963145   ReqAmt    5763674.354
## 72              8 0.9719388   ReqAmt  128634877.768
## 73             11 0.9393939   ReqAmt     307161.315
## 74             15 0.9877778   ReqAmt    1654630.318
## 75              9 0.9081197   ReqAmt  229140190.814
## 76             17 0.9416620   ReqAmt    4008969.074
## 77              1 1.0000000   ReqAmt     102651.430
## 78             11 1.0000000   ReqAmt     443989.310
## 79              2 0.8750000   ReqAmt     272460.020
## 80              3 1.0000000   ReqAmt    1108201.320
## 81              2 1.0000000   ReqAmt     104938.200
## 82             10 0.9485833   ReqAmt    4512388.537
## 83              4 0.9024621   ReqAmt   37976680.957
## 84              1 1.0000000   ReqAmt        286.360
## 85              1 1.0000000   ReqAmt       2653.770
## 86              8 0.8577398   ReqAmt   50743081.963
## 87              1 1.0000000   ReqAmt       1182.960
## 88             29 0.9718870   ReqAmt  316300288.755
## 89              1 1.0000000   ReqAmt       2723.090
## 90              3 1.0000000   ReqAmt    2086165.980
## 91              2 1.0000000   ReqAmt     180637.230
## 92              1 1.0000000   ReqAmt      29018.400
## 93              2 1.0000000   ReqAmt     271707.530
## 94              5 0.9577160   ReqAmt  208865661.603
## 95              5 0.8254386   ReqAmt   35062682.416
## 96              2 0.8750000   ReqAmt      45199.790
## 97              1 1.0000000   ReqAmt       6603.170
## 98              1 1.0000000   ReqAmt     232262.580
## 99              7 0.9467419   ReqAmt   45226383.104
## 100             2 1.0000000   ReqAmt      46008.880
## 101             1 1.0000000   ReqAmt    1018002.830
## 102             3 1.0000000   ReqAmt     687623.480
## 103            12 0.9692460   ReqAmt    3512938.729
## 104            11 1.0000000   ReqAmt     280939.450
## 105             3 1.0000000   ReqAmt     211961.670
## 106            17 0.9526612   ReqAmt   87709869.791
## 107             7 0.9821429   ReqAmt       1008.840
## 108            20 0.9642708   ReqAmt   10618838.020
## 109            32 0.9880528   ReqAmt  395544734.495
## 110             2 1.0000000   ReqAmt      18783.640
## 111            22 0.8891936   ReqAmt   88887842.222
## 112            10 1.0000000   ReqAmt      86335.010
## 113            10 1.0000000   ReqAmt     424387.650
## 114            12 1.0000000   ReqAmt    5771336.070
## 115             3 1.0000000   ReqAmt     509269.170
## 116             5 0.9375000   ReqAmt       1149.980
## 117             7 0.8925463   ReqAmt  161422456.144
## 118            12 0.9670139   ReqAmt       1713.930
## 119             4 1.0000000   ReqAmt       2425.580
## 120             2 1.0000000   ReqAmt     236987.220
## 121             3 1.0000000   ReqAmt     524921.740
## 122             3 1.0000000   ReqAmt     119947.680
## 123            11 0.9589371   ReqAmt   96529491.803
## 124             2 1.0000000   ReqAmt     260036.530
## 125            12 0.9625000   ReqAmt   12554411.200
## 126             2 1.0000000   ReqAmt     134966.980
## 127             2 0.8750000   ReqAmt      82661.270
## 128            11 0.9412578   ReqAmt  146321455.050
## 129             1 1.0000000   ReqAmt     346200.000
## 130             1 1.0000000   ReqAmt    1168689.180
## 131             3 0.9166667   ReqAmt     352250.492
## 132             2 1.0000000   ReqAmt     103137.600
## 133             1 0.7500000   ReqAmt       1341.345
## 134             3 1.0000000   ReqAmt    2289982.840
## 135             2 1.0000000   ReqAmt    1163854.640
## 136             1 1.0000000   ReqAmt       5290.460
## 137             1 1.0000000   ReqAmt     387650.630
## 138             1 1.0000000   ReqAmt      25500.000
## 139             4 1.0000000   ReqAmt      24968.320
## 140             7 0.8838037   ReqAmt   42212935.796
## 141             4 1.0000000   ReqAmt    1470774.280
## 142             1 1.0000000   ReqAmt       1949.950
## 143             5 1.0000000   ReqAmt     835516.430
## 144            14 0.9339262   ReqAmt  121502081.226
## 145             4 0.9250000   ReqAmt    2532262.503
## 146             2 1.0000000   ReqAmt     283095.560
## 147             4 0.9000000   ReqAmt     625362.566
## 148             2 0.9375000   ReqAmt     790951.948
## 149             8 0.9722222   ReqAmt    1234320.114
## 150             3 0.9583333   ReqAmt     415740.236
## 151             7 0.9785714   ReqAmt    1101231.112
## 152             3 1.0000000   ReqAmt     254743.430
## 153             2 1.0000000   ReqAmt     273892.820
## 154             1 1.0000000   ReqAmt       3814.350
## 155             1 1.0000000   ReqAmt     308702.950
## 156             5 0.8455278   ReqAmt  217418030.303
## 157             1 1.0000000   ReqAmt     118977.370
## 158             1 1.0000000   ReqAmt         78.240
## 159             6 0.8928571   ReqAmt   32247036.357
## 160             1 1.0000000   ReqAmt     364942.000
## 161             3 0.9583333   ReqAmt    1140454.008
## 162             3 1.0000000   ReqAmt     154445.340
## 163             4 1.0000000   ReqAmt     666950.560
## 164            21 0.9570023   ReqAmt   51944911.988
## 165             9 0.9176731   ReqAmt  113207921.345
## 166            12 0.9849850   ReqAmt    7120007.749
## 167             2 1.0000000   ReqAmt     263050.590
## 168             2 1.0000000   ReqAmt      90192.220
## 169             1 1.0000000   ReqAmt       6202.280
## 170             2 1.0000000   ReqAmt      19415.820
## 171             8 0.9427083   ReqAmt      67117.355
## 172             3 0.7865497   ReqAmt   49769771.017
## 173             1 1.0000000   ReqAmt      50799.880
## 174             1 1.0000000   ReqAmt      77921.730
## 175             3 0.8345960   ReqAmt   29947514.345
## 176             1 1.0000000   ReqAmt       3676.840
## 177            11 0.9576923   ReqAmt   10644894.488
## 178             1 1.0000000   ReqAmt      20544.030
## 179             2 1.0000000   ReqAmt      22788.670
## 180             2 1.0000000   ReqAmt     265274.050
## 181             4 0.7834154   ReqAmt   21206585.948
## 182            28 0.9777722   ReqAmt   24127007.028
## 183            22 0.9735742   ReqAmt  155275737.024
## 184             8 0.9843750   ReqAmt    4571346.350
## 185             1 0.9117647   ReqAmt      79832.021
## 186             4 1.0000000   ReqAmt     273022.050
## 187             8 0.8347631   ReqAmt   65519432.546
## 188            15 0.9623679   ReqAmt  108482263.892
## 189             1 1.0000000   ReqAmt      36192.200
## 190             2 1.0000000   ReqAmt     121394.250
## 191             1 1.0000000   ReqAmt     120000.000
## 192            12 0.9339213   ReqAmt  692794601.686
## 193            37 0.9683633   ReqAmt  321823123.667
## 194             1 1.0000000   ReqAmt     959466.420
## 195            19 0.9901316   ReqAmt    4274073.345
## 196             1 1.0000000   ReqAmt       2518.010
## 197             7 0.9788265   ReqAmt  111126098.176
## 198             2 0.8846154   ReqAmt     636387.600
## 199             2 1.0000000   ReqAmt     227737.820
## 200             3 1.0000000   ReqAmt     370101.420
## 201             3 1.0000000   ReqAmt    3302894.060
## 202             1 1.0000000   ReqAmt     152115.100
## 203            14 0.9637363   ReqAmt    1384144.303
## 204             1 1.0000000   ReqAmt     195000.000
## 205             6 1.0000000   ReqAmt    8610578.300
## 206             1 1.0000000   ReqAmt     630000.000
## 207             1 1.0000000   ReqAmt    2059419.360
## 208             1 0.8750000   ReqAmt      52500.000
## 209             2 1.0000000   ReqAmt    2594238.880
## 210             7 0.8762690   ReqAmt   45850122.278
## 211             2 1.0000000   ReqAmt      25036.250
## 212             4 1.0000000   ReqAmt    2737835.670
## 213             4 1.0000000   ReqAmt     534319.620
## 214             1 1.0000000   ReqAmt      20309.260
## 215             1 1.0000000   ReqAmt       1765.860
## 216             1 1.0000000   ReqAmt        769.530
## 217            20 0.9507279   ReqAmt   40286019.999
## 218             2 1.0000000   ReqAmt      84833.770
## 219            22 0.9449364   ReqAmt  101507136.328
## 220             2 1.0000000   ReqAmt      44462.520
## 221             1 1.0000000   ReqAmt       3107.730
## 222             5 0.9586207   ReqAmt    1662495.725
## 223             2 1.0000000   ReqAmt     893053.000
## 224            19 0.9708271   ReqAmt   94574335.387
## 225             3 1.0000000   ReqAmt     764707.950
## 226             4 0.9843750   ReqAmt     374325.541
## 227            18 0.9757344   ReqAmt     989615.512
## 228             1 1.0000000   ReqAmt      15332.800
## 229            13 0.9290244   ReqAmt    4672306.929
## 230             1 1.0000000   ReqAmt      60044.110
## 231             6 0.8756313   ReqAmt  211301981.278
## 232            15 1.0000000   ReqAmt    1568817.560
## 233            55 0.9978931   ReqAmt 4388540486.393
## 234            17 0.9782353   ReqAmt    6576000.337
## 235             7 0.9875000   ReqAmt       1843.730
## 236             1 1.0000000   ReqAmt     178500.000
## 237             1 1.0000000   ReqAmt       2349.600
## 238             1 1.0000000   ReqAmt       9919.830
## 239             3 1.0000000   ReqAmt     133425.920
## 240            23 0.9868961   ReqAmt   53719536.406
## 241             5 1.0000000   ReqAmt      23633.170
## 242             5 0.9500000   ReqAmt      66960.307
## 243            18 0.9634648   ReqAmt    4778037.892
## 244            17 0.9685143   ReqAmt    3353754.898
## 245             1 1.0000000   ReqAmt      14578.300
## 246             4 0.9062500   ReqAmt      45308.778
## 247             9 0.9722222   ReqAmt    2988161.275
## 248             5 0.8750000   ReqAmt  126759974.990
## 249             8 1.0000000   ReqAmt    1236104.410
## 250             3 0.7698413   ReqAmt   22620889.705
## 251             4 1.0000000   ReqAmt      25909.690
## 252             2 1.0000000   ReqAmt       6298.040
## 253             1 1.0000000   ReqAmt      26177.280
## 254             3 1.0000000   ReqAmt     174774.270
## 255             2 1.0000000   ReqAmt      48765.680
## 256             1 1.0000000   ReqAmt    1019835.900
## 257             1 0.7500000   ReqAmt       6651.495
## 258             4 1.0000000   ReqAmt    2112776.860
## 259             6 0.7916667   ReqAmt     287876.965
## 260             1 1.0000000   ReqAmt     490433.540
## 261             8 0.9174020   ReqAmt  164632258.112
## 262             1 1.0000000   ReqAmt     353235.050
## 263             1 1.0000000   ReqAmt       3970.870
## 264             3 0.9215686   ReqAmt     902026.824
## 265             1 1.0000000   ReqAmt     154605.100
## 266            14 0.8973214   ReqAmt    8170293.791
## 267            11 0.9772727   ReqAmt    1621471.140
## 268             1 1.0000000   ReqAmt     133625.850
## 269             4 1.0000000   ReqAmt    6923426.390
## 270             3 0.9411765   ReqAmt     786880.266
## 271             1 1.0000000   ReqAmt     736916.440
## 272             6 0.9166667   ReqAmt    2543426.732
## 273             3 1.0000000   ReqAmt      23168.720
## 274             3 1.0000000   ReqAmt     605529.730
## 275            41 0.9790543   ReqAmt  243959878.197
## 276             1 1.0000000   ReqAmt       9963.280
## 277             3 0.8888889   ReqAmt     215670.600
## 278             2 1.0000000   ReqAmt      28294.890
## 279             1 1.0000000   ReqAmt     154452.600
## 280            14 0.9253266   ReqAmt  502364289.623
## 281             4 1.0000000   ReqAmt      34736.250
## 282             6 0.8253086   ReqAmt   60451184.634
## 283             2 1.0000000   ReqAmt     160794.760
## 284             1 1.0000000   ReqAmt     127930.750
## 285             1 1.0000000   ReqAmt        252.380
## 286            15 0.9273061   ReqAmt   71890050.048
## 287             2 1.0000000   ReqAmt      10474.070
## 288             5 0.9500000   ReqAmt    1374781.090
## 289             2 1.0000000   ReqAmt       7907.510
## 290             2 0.6750000   ReqAmt       2779.972
## 291             6 0.9204545   ReqAmt   10027084.889
## 292             1 1.0000000   ReqAmt     157715.930
## 293             2 1.0000000   ReqAmt      19981.720
## 294             1 1.0000000   ReqAmt       3875.840
## 295            20 0.9393999   ReqAmt  156581635.950
## 296             2 1.0000000   ReqAmt      46503.900
## 297             1 0.8750000   ReqAmt      35751.327
## 298             2 1.0000000   ReqAmt      41114.910
## 299             7 0.9821429   ReqAmt      32608.135
## 300            11 0.8621212   ReqAmt     464901.590
## 301             7 0.9083333   ReqAmt     698415.353
## 302             3 0.8333333   ReqAmt      52088.308
## 303            11 0.8260511   ReqAmt    2618715.592
## 304             2 1.0000000   ReqAmt      12026.080
## 305             4 1.0000000   ReqAmt    1424406.980
## 306             2 1.0000000   ReqAmt      51697.680
## 307            12 0.9090799   ReqAmt  146171759.829
## 308             1 1.0000000   ReqAmt     226377.290
## 309             4 1.0000000   ReqAmt    1475482.050
## 310             2 1.0000000   ReqAmt       9947.230
## 311             2 1.0000000   ReqAmt      77864.330
## 312             4 0.9375000   ReqAmt    1136517.277
## 313            12 0.9727564   ReqAmt    1458730.028
## 314             1 1.0000000   ReqAmt     246509.220
## 315             1 1.0000000   ReqAmt      43583.480
## 316             3 0.9166667   ReqAmt    1284263.375
## 317             2 0.9583333   ReqAmt      32516.559
## 318             6 0.8944820   ReqAmt  102157245.195
## 319             2 1.0000000   ReqAmt      56504.980
## 320             1 1.0000000   ReqAmt       2649.160
## 321             4 1.0000000   ReqAmt     134885.370
## 322             5 0.9250000   ReqAmt    8636910.953
## 323             3 1.0000000   ReqAmt     188671.410
## 324            10 0.9766411   OblAmt    2434844.857
## 325             8 0.7550000   OblAmt   19863078.852
## 326             6 1.0000000   OblAmt    5197162.960
## 327             1 1.0000000   OblAmt       1846.870
## 328            10 0.9551913   OblAmt   35717008.133
## 329             2 1.0000000   OblAmt     201660.260
## 330             4 1.0000000   OblAmt     957840.990
## 331             2 1.0000000   OblAmt     113969.080
## 332             2 1.0000000   OblAmt     637446.550
## 333             5 1.0000000   OblAmt     308420.770
## 334             2 1.0000000   OblAmt      16978.890
## 335             4 1.0000000   OblAmt     639155.790
## 336             1 1.0000000   OblAmt     113021.520
## 337             5 0.9391026   OblAmt    8963656.662
## 338             3 1.0000000   OblAmt    1207244.700
## 339             3 1.0000000   OblAmt      25315.020
## 340             8 0.8936012   OblAmt    3529626.690
## 341             9 0.9198390   OblAmt  162666766.716
## 342             4 0.9062500   OblAmt     644115.000
## 343            17 0.9559613   OblAmt  412969248.115
## 344            13 0.9871795   OblAmt      73939.550
## 345             9 1.0000000   OblAmt     111265.000
## 346             1 1.0000000   OblAmt     315991.420
## 347            14 0.9341270   OblAmt      38242.252
## 348             8 0.9285714   OblAmt    6053446.853
## 349             1 1.0000000   OblAmt      64018.400
## 350            16 1.0000000   OblAmt     210464.360
## 351             8 0.9816176   OblAmt   58462371.498
## 352            17 0.9658304   OblAmt   28294846.186
## 353            19 0.9164789   OblAmt   53400370.670
## 354             8 0.9513889   OblAmt    8672055.556
## 355            10 1.0000000   OblAmt      42664.260
## 356             2 1.0000000   OblAmt     216446.440
## 357             9 1.0000000   OblAmt       9933.690
## 358             9 0.9715414   OblAmt    4132990.190
## 359            17 0.9628422   OblAmt  670111993.343
## 360            12 0.9599868   OblAmt    7207299.594
## 361             2 1.0000000   OblAmt    1721710.130
## 362             3 1.0000000   OblAmt     221173.140
## 363             4 1.0000000   OblAmt       3653.860
## 364            13 0.9331854   OblAmt   80554165.268
## 365            11 0.9738636   OblAmt     555593.710
## 366            19 0.9717086   OblAmt    8533396.606
## 367             1 0.7500000   OblAmt    1130890.200
## 368             3 0.8272198   OblAmt   33230911.308
## 369            15 0.9916667   OblAmt    1280811.180
## 370             2 1.0000000   OblAmt     132717.390
## 371             2 1.0000000   OblAmt      15267.200
## 372            15 0.9958333   OblAmt    1306839.321
## 373             4 0.7500000   OblAmt   24163037.250
## 374            15 1.0000000   OblAmt    1388233.930
## 375             9 0.9537701   OblAmt   29138452.633
## 376             5 0.8333333   OblAmt     810984.676
## 377             3 0.9444444   OblAmt     241737.035
## 378             7 0.9553571   OblAmt    2232723.088
## 379             3 1.0000000   OblAmt       2666.200
## 380             1 1.0000000   OblAmt       5402.120
## 381             4 0.8750000   OblAmt    1374983.335
## 382             5 0.9500000   OblAmt     107678.230
## 383             4 1.0000000   OblAmt      15134.030
## 384            25 0.9681893   OblAmt  206435753.579
## 385             2 1.0000000   OblAmt      24006.000
## 386             6 0.9045940   OblAmt  212878073.490
## 387            22 0.9575652   OblAmt   65488854.663
## 388            17 0.9440368   OblAmt    2451923.077
## 389             1 1.0000000   OblAmt      15517.410
## 390             2 1.0000000   OblAmt     105956.560
## 391             3 0.8622449   OblAmt  125561025.953
## 392             8 1.0000000   OblAmt     953479.950
## 393             1 1.0000000   OblAmt     782876.140
## 394            22 0.9963145   OblAmt    5763674.354
## 395             8 0.9719388   OblAmt  128634877.768
## 396            11 0.9393939   OblAmt     307161.315
## 397            15 0.9877778   OblAmt    1654630.318
## 398             9 0.9081197   OblAmt  229140190.814
## 399            17 0.9416620   OblAmt    4008969.074
## 400             1 1.0000000   OblAmt     102651.430
## 401            11 1.0000000   OblAmt     443989.310
## 402             2 0.8750000   OblAmt     272460.020
## 403             3 1.0000000   OblAmt    1108201.320
## 404             2 1.0000000   OblAmt     104938.200
## 405            10 0.9485833   OblAmt    4512388.537
## 406             4 0.9024621   OblAmt   37976680.957
## 407             1 1.0000000   OblAmt        286.360
## 408             1 1.0000000   OblAmt       2653.770
## 409             8 0.8577398   OblAmt   50743081.963
## 410             1 1.0000000   OblAmt       1182.960
## 411            29 0.9718870   OblAmt  316300288.755
## 412             1 1.0000000   OblAmt       2723.090
## 413             3 1.0000000   OblAmt    2086165.980
## 414             2 1.0000000   OblAmt     180637.230
## 415             1 1.0000000   OblAmt      29018.400
## 416             2 1.0000000   OblAmt     271707.530
## 417             5 0.9577160   OblAmt  208865661.603
## 418             5 0.8254386   OblAmt   35062682.416
## 419             2 0.8750000   OblAmt      45199.790
## 420             1 1.0000000   OblAmt       6603.170
## 421             1 1.0000000   OblAmt     232262.580
## 422             7 0.9467419   OblAmt   45226383.104
## 423             2 1.0000000   OblAmt      46008.880
## 424             1 1.0000000   OblAmt    1018002.830
## 425             3 1.0000000   OblAmt     687623.480
## 426            12 0.9692460   OblAmt    3512938.729
## 427            11 1.0000000   OblAmt     280939.450
## 428             3 1.0000000   OblAmt     211961.670
## 429            17 0.9526612   OblAmt   87709869.791
## 430             7 0.9821429   OblAmt       1008.840
## 431            20 0.9642708   OblAmt   10618838.020
## 432            32 0.9880528   OblAmt  395544734.495
## 433             2 1.0000000   OblAmt      18783.640
## 434            22 0.8891936   OblAmt   88887842.222
## 435            10 1.0000000   OblAmt      86335.010
## 436            10 1.0000000   OblAmt     424387.650
## 437            12 1.0000000   OblAmt    5771336.070
## 438             3 1.0000000   OblAmt     509269.170
## 439             5 0.9375000   OblAmt       1149.980
## 440             7 0.8925463   OblAmt  161422456.144
## 441            12 0.9670139   OblAmt       1713.930
## 442             4 1.0000000   OblAmt       2425.580
## 443             2 1.0000000   OblAmt     236987.220
## 444             3 1.0000000   OblAmt     524921.740
## 445             3 1.0000000   OblAmt     119947.680
## 446            11 0.9589371   OblAmt   96529491.803
## 447             2 1.0000000   OblAmt     260036.530
## 448            12 0.9625000   OblAmt   12554411.200
## 449             2 1.0000000   OblAmt     134966.980
## 450             2 0.8750000   OblAmt      82661.270
## 451            11 0.9412578   OblAmt  146321455.050
## 452             1 1.0000000   OblAmt     346200.000
## 453             1 1.0000000   OblAmt    1168689.180
## 454             3 0.9166667   OblAmt     352250.492
## 455             2 1.0000000   OblAmt     103137.600
## 456             1 0.7500000   OblAmt       1341.345
## 457             3 1.0000000   OblAmt    2289982.840
## 458             2 1.0000000   OblAmt    1163854.640
## 459             1 1.0000000   OblAmt       5290.460
## 460             1 1.0000000   OblAmt     387650.630
## 461             1 1.0000000   OblAmt      25500.000
## 462             4 1.0000000   OblAmt      24968.320
## 463             7 0.8838037   OblAmt   42212935.796
## 464             4 1.0000000   OblAmt    1470774.280
## 465             1 1.0000000   OblAmt       1949.950
## 466             5 1.0000000   OblAmt     835516.430
## 467            14 0.9339262   OblAmt  121954462.178
## 468             4 0.9250000   OblAmt    2532262.503
## 469             2 1.0000000   OblAmt     283095.560
## 470             4 0.9000000   OblAmt     625362.566
## 471             2 0.9375000   OblAmt     790951.948
## 472             8 0.9722222   OblAmt    1234320.114
## 473             3 0.9583333   OblAmt     415740.236
## 474             7 0.9785714   OblAmt    1101231.112
## 475             3 1.0000000   OblAmt     254743.430
## 476             2 1.0000000   OblAmt     273892.820
## 477             1 1.0000000   OblAmt       3814.350
## 478             1 1.0000000   OblAmt     308702.950
## 479             5 0.8455278   OblAmt  217418030.303
## 480             1 1.0000000   OblAmt     118977.370
## 481             1 1.0000000   OblAmt         78.240
## 482             6 0.8928571   OblAmt   32247036.357
## 483             1 1.0000000   OblAmt     364942.000
## 484             3 0.9583333   OblAmt    1140454.008
## 485             3 1.0000000   OblAmt     154445.340
## 486             4 1.0000000   OblAmt     666950.560
## 487            21 0.9570023   OblAmt   51944911.988
## 488             9 0.9176731   OblAmt  113207921.345
## 489            12 0.9849850   OblAmt    7120007.749
## 490             2 1.0000000   OblAmt     263050.590
## 491             2 1.0000000   OblAmt      90192.220
## 492             1 1.0000000   OblAmt       6202.280
## 493             2 1.0000000   OblAmt      19415.820
## 494             8 0.9427083   OblAmt      67117.355
## 495             3 0.7865497   OblAmt   49769771.017
## 496             1 1.0000000   OblAmt      50799.880
## 497             1 1.0000000   OblAmt      77921.730
## 498             3 0.8345960   OblAmt   29947514.345
## 499             1 1.0000000   OblAmt       3676.840
## 500            11 0.9576923   OblAmt   10894894.488
## 501             1 1.0000000   OblAmt      20544.030
## 502             2 1.0000000   OblAmt      22788.670
## 503             2 1.0000000   OblAmt     265274.050
## 504             4 0.7834154   OblAmt   21206585.948
## 505            28 0.9777722   OblAmt   33454539.256
## 506            22 0.9735742   OblAmt  155275737.024
## 507             8 0.9843750   OblAmt    4571346.350
## 508             1 0.9117647   OblAmt      79832.021
## 509             4 1.0000000   OblAmt     273022.050
## 510             8 0.8347631   OblAmt   65519432.546
## 511            15 0.9623679   OblAmt  108482263.892
## 512             1 1.0000000   OblAmt      36192.200
## 513             2 1.0000000   OblAmt     121394.250
## 514             1 1.0000000   OblAmt     120000.000
## 515            12 0.9339213   OblAmt  692794601.686
## 516            37 0.9683633   OblAmt  321823123.667
## 517             1 1.0000000   OblAmt     959466.420
## 518            19 0.9901316   OblAmt    4274073.345
## 519             1 1.0000000   OblAmt       2518.010
## 520             7 0.9788265   OblAmt  111126098.176
## 521             2 0.8846154   OblAmt     636387.600
## 522             2 1.0000000   OblAmt     227737.820
## 523             3 1.0000000   OblAmt     370101.420
## 524             3 1.0000000   OblAmt    3302894.060
## 525             1 1.0000000   OblAmt     152115.100
## 526            14 0.9637363   OblAmt    1384144.303
## 527             1 1.0000000   OblAmt     195000.000
## 528             6 1.0000000   OblAmt    8610578.300
## 529             1 1.0000000   OblAmt     630000.000
## 530             1 1.0000000   OblAmt    2059419.360
## 531             1 0.8750000   OblAmt      52500.000
## 532             2 1.0000000   OblAmt    2594238.880
## 533             7 0.8762690   OblAmt   45850122.278
## 534             2 1.0000000   OblAmt      25036.250
## 535             4 1.0000000   OblAmt    2737835.670
## 536             4 1.0000000   OblAmt     534319.620
## 537             1 1.0000000   OblAmt      20309.260
## 538             1 1.0000000   OblAmt       1765.860
## 539             1 1.0000000   OblAmt        769.530
## 540            20 0.9507279   OblAmt   40286019.999
## 541             2 1.0000000   OblAmt      84833.770
## 542            22 0.9449364   OblAmt  101507136.328
## 543             2 1.0000000   OblAmt      44462.520
## 544             1 1.0000000   OblAmt       3107.730
## 545             5 0.9586207   OblAmt    1662495.725
## 546             2 1.0000000   OblAmt     893053.000
## 547            19 0.9708271   OblAmt   94574335.387
## 548             3 1.0000000   OblAmt     764707.950
## 549             4 0.9843750   OblAmt     374325.541
## 550            18 0.9757344   OblAmt     989615.512
## 551             1 1.0000000   OblAmt      15332.800
## 552            13 0.9290244   OblAmt    4672306.929
## 553             1 1.0000000   OblAmt      60044.110
## 554             6 0.8756313   OblAmt  211301981.278
## 555            15 1.0000000   OblAmt    1568817.560
## 556            55 0.9978931   OblAmt 4408424297.392
## 557            17 0.9782353   OblAmt    6576000.337
## 558             7 0.9875000   OblAmt       1843.730
## 559             1 1.0000000   OblAmt     178500.000
## 560             1 1.0000000   OblAmt       2349.600
## 561             1 1.0000000   OblAmt       9919.830
## 562             3 1.0000000   OblAmt     133425.920
## 563            23 0.9868961   OblAmt   53719536.406
## 564             5 1.0000000   OblAmt      23633.170
## 565             5 0.9500000   OblAmt      66960.307
## 566            18 0.9634648   OblAmt    4678037.892
## 567            17 0.9685143   OblAmt    3368709.464
## 568             1 1.0000000   OblAmt      14578.300
## 569             4 0.9062500   OblAmt      45308.778
## 570             9 0.9722222   OblAmt    2988161.275
## 571             5 0.8750000   OblAmt  126759974.990
## 572             8 1.0000000   OblAmt    1236104.410
## 573             3 0.7698413   OblAmt   22620889.705
## 574             4 1.0000000   OblAmt      25909.690
## 575             2 1.0000000   OblAmt       6298.040
## 576             1 1.0000000   OblAmt      26177.280
## 577             3 1.0000000   OblAmt     174774.270
## 578             2 1.0000000   OblAmt      48765.680
## 579             1 1.0000000   OblAmt    1019835.900
## 580             1 0.7500000   OblAmt       6651.495
## 581             4 1.0000000   OblAmt    2112776.860
## 582             6 0.7916667   OblAmt     287876.965
## 583             1 1.0000000   OblAmt     490433.540
## 584             8 0.9174020   OblAmt  164632258.112
## 585             1 1.0000000   OblAmt     353235.050
## 586             1 1.0000000   OblAmt       3970.870
## 587             3 0.9215686   OblAmt     902026.824
## 588             1 1.0000000   OblAmt     154605.100
## 589            14 0.8973214   OblAmt    8170293.791
## 590            11 0.9772727   OblAmt    1688230.510
## 591             1 1.0000000   OblAmt     133625.850
## 592             4 1.0000000   OblAmt    7172730.750
## 593             3 0.9411765   OblAmt    1167691.726
## 594             1 1.0000000   OblAmt     736916.440
## 595             6 0.9166667   OblAmt    2543426.732
## 596             3 1.0000000   OblAmt      23168.720
## 597             3 1.0000000   OblAmt     605529.730
## 598            41 0.9790543   OblAmt  244346590.682
## 599             1 1.0000000   OblAmt       9963.280
## 600             3 0.8888889   OblAmt     215670.600
## 601             2 1.0000000   OblAmt      28294.890
## 602             1 1.0000000   OblAmt     154452.600
## 603            14 0.9253266   OblAmt  502364289.623
## 604             4 1.0000000   OblAmt      34736.250
## 605             6 0.8253086   OblAmt   60451184.634
## 606             2 1.0000000   OblAmt     160794.760
## 607             1 1.0000000   OblAmt     127930.750
## 608             1 1.0000000   OblAmt        252.380
## 609            15 0.9273061   OblAmt   71890050.071
## 610             2 1.0000000   OblAmt      10474.070
## 611             5 0.9500000   OblAmt    1374781.090
## 612             2 1.0000000   OblAmt       7907.510
## 613             2 0.6750000   OblAmt       2779.972
## 614             6 0.9204545   OblAmt   10027084.889
## 615             1 1.0000000   OblAmt     157715.930
## 616             2 1.0000000   OblAmt      19981.720
## 617             1 1.0000000   OblAmt       3875.840
## 618            20 0.9393999   OblAmt  156581635.950
## 619             2 1.0000000   OblAmt      46503.900
## 620             1 0.8750000   OblAmt      35751.327
## 621             2 1.0000000   OblAmt      41114.910
## 622             7 0.9821429   OblAmt      32608.135
## 623            11 0.8621212   OblAmt     464901.590
## 624             7 0.9083333   OblAmt     698415.353
## 625             3 0.8333333   OblAmt      52088.308
## 626            11 0.8260511   OblAmt    2618715.592
## 627             2 1.0000000   OblAmt      12026.080
## 628             4 1.0000000   OblAmt    1424406.980
## 629             2 1.0000000   OblAmt      51697.680
## 630            12 0.9090799   OblAmt  146171759.829
## 631             1 1.0000000   OblAmt     226377.290
## 632             4 1.0000000   OblAmt    1475482.050
## 633             2 1.0000000   OblAmt       9947.230
## 634             2 1.0000000   OblAmt      77864.330
## 635             4 0.9375000   OblAmt    1136517.277
## 636            12 0.9727564   OblAmt    1469478.818
## 637             1 1.0000000   OblAmt     246509.220
## 638             1 1.0000000   OblAmt      43583.480
## 639             3 0.9166667   OblAmt    1284263.375
## 640             2 0.9583333   OblAmt      32516.559
## 641             6 0.8944820   OblAmt  102157245.195
## 642             2 1.0000000   OblAmt      56504.980
## 643             1 1.0000000   OblAmt       2649.160
## 644             4 1.0000000   OblAmt     134885.370
## 645             5 0.9250000   OblAmt    8636910.953
## 646             3 1.0000000   OblAmt     188671.410
#change labels
levels(data_long$FundType) <- c("Requested Amount", "Obligated Amount")

#now usual ggplot2 density plot
funds_density <- ggplot2::ggplot(data = data_long, aes(x = log(funds), color = FundType, fill = FundType)) +
                          geom_density(alpha = 0.4) +
                          facet_wrap(~ FundType) +
                          scale_fill_manual(values = c("#6ece58", "#26828e")) +
                          scale_color_manual(values = c("#6ece58", "#26828e")) +
                          labs(x = "Log(Funding)",
                               y = "Density") +
                          theme(legend.position="none")

funds_density

#save file
funds_density_fig_file = here("results","funds_density.png")
ggsave(filename = funds_density_fig_file, plot = funds_density)
## Saving 7 x 5 in image

Predictor: State

#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$state, report.nas = FALSE)
## Frequencies  
## EDAdata_outliers$state  
## Label: State Abbreviation  
## Type: Character  
## 
##               Freq        %   % Cum.
## ----------- ------ -------- --------
##          AK      4     1.24     1.24
##          AL      4     1.24     2.48
##          AR      8     2.48     4.95
##          AZ      3     0.93     5.88
##          CA     19     5.88    11.76
##          CO      5     1.55    13.31
##          CT      6     1.86    15.17
##          DC      2     0.62    15.79
##          DE      1     0.31    16.10
##          FL     13     4.02    20.12
##          GA      8     2.48    22.60
##          HI      5     1.55    24.15
##          IA      5     1.55    25.70
##          ID      4     1.24    26.93
##          IL      4     1.24    28.17
##          IN      3     0.93    29.10
##          KS      3     0.93    30.03
##          KY      5     1.55    31.58
##          LA     16     4.95    36.53
##          MA      6     1.86    38.39
##          ME      2     0.62    39.01
##          MI      6     1.86    40.87
##          MN     10     3.10    43.96
##          MO      7     2.17    46.13
##          MS      8     2.48    48.61
##          MT      2     0.62    49.23
##          NC      9     2.79    52.01
##          ND      4     1.24    53.25
##          NE      5     1.55    54.80
##          NH      4     1.24    56.04
##          NJ      3     0.93    56.97
##          NM      3     0.93    57.89
##          NV      3     0.93    58.82
##          NY      6     1.86    60.68
##          OH      6     1.86    62.54
##          OK     10     3.10    65.63
##          OR      8     2.48    68.11
##          PA      6     1.86    69.97
##          PR      9     2.79    72.76
##          RI      5     1.55    74.30
##          SC      8     2.48    76.78
##          SD     10     3.10    79.88
##          TN      7     2.17    82.04
##          TX     16     4.95    87.00
##          UT      1     0.31    87.31
##          VA      6     1.86    89.16
##          VT      4     1.24    90.40
##          WA     11     3.41    93.81
##          WI      8     2.48    96.28
##          WV     10     3.10    99.38
##          WY      2     0.62   100.00
##       Total    323   100.00   100.00
#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#filter top 5 states
#start with obligated amount
obl_fig_state <- EDAdata_outliers %>%
                    dplyr::add_count(state) %>%
                    dplyr::filter(dplyr::dense_rank(-n) < 5) %>%
                    ggplot2::ggplot(aes(x = state, y = OblAmt)) +
                      geom_boxplot(color = "#440154") +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      labs(x = "State or Territory",
                           y = "Obligated FEMA Funds")
obl_fig_state

#repeat for requested funds
req_fig_state <- EDAdata_outliers %>%
                    dplyr::add_count(state) %>%
                    dplyr::filter(dplyr::dense_rank(-n) < 6) %>%
                    ggplot2::ggplot(aes(x = state, y = ReqAmt)) +
                      geom_boxplot(color = "#440154") +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      labs(x = " " ,
                           y = "Requested FEMA Funds")
req_fig_state

#combine into one plot
plot_states <- cowplot::plot_grid(req_fig_state, 
                                     obl_fig_state,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
plot_states

title_state <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs Obligated FEMA Funds For Five Most Disaster Prone States",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme(plot.margin = margin(0, 0, 0, 7))

plot_states_funds <- cowplot::plot_grid(
                              title_state, plot_states,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
plot_states_funds

#save file
states_fig_file = here("results","states-funds.png")
ggsave(filename = states_fig_file, plot = plot_states_funds)
## Saving 7 x 5 in image
#create a table where columns represent requested and obligated funds and rows are 5 most disaster-prone states
decs_state_5 <- EDAdata_outliers %>%
                    dplyr::add_count(state) %>%
                    dplyr::filter(dense_rank(-n) < 6) %>%
                    gtsummary::select(state, ReqAmt, OblAmt) %>%
                    gtsummary::tbl_summary(
                      by = state,
                      label = list(state ~ "State or Territory",
                                   ReqAmt ~ "Requested Funds",
                                   OblAmt ~ "Obligated Funds"),
                      statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
                    gtsummary::modify_header(label ~ "**Variable**") %>% 
                    gtsummary::modify_spanning_header(c("stat_1", "stat_2", "stat_3", "stat_4", "stat_5") ~ "**State**") %>%
                    gtsummary::modify_caption("**Table 4. FEMA Funding for 5 Most Disaster-Prone States 2012 - 2021**") %>%
                    gtsummary::bold_labels()
decs_state_5
Table 4. FEMA Funding for 5 Most Disaster-Prone States 2012 - 2021
Variable State SD, N = 101 TX, N = 161 WA, N = 111 WV, N = 101
CA, N = 191 FL, N = 131 LA, N = 161 MN, N = 101 OK, N = 101
Requested Funds 65,900,489 (173,898,625) 37,851,532 (78,320,708) 47,187,723 (104,127,805) 4,758,425 (13,184,247) 6,413,888 (14,086,311) 2,727,818 (7,026,623) 48,019,502 (135,429,624) 14,599,599 (47,096,442) 10,655,340 (32,155,687)
Obligated Funds 65,900,489 (173,898,625) 37,851,532 (78,320,708) 47,187,723 (104,127,805) 4,758,425 (13,184,247) 6,413,888 (14,086,311) 2,727,818 (7,026,623) 48,087,226 (135,451,571) 14,599,599 (47,096,442) 10,656,415 (32,155,346)

1 Mean (SD)

#save file
decs_state_file = here("results", "decs-state-5.Rds")
saveRDS(decs_state_5, file = decs_state_file)

Predictor: Incident Year

#summary statistics since it's continuous
base::summary(EDAdata_outliers$IncidentYear)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2012    2015    2017    2017    2020    2021
#examine graphical relationship with outcomes

#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = IncidentYear)) +
  geom_density()

#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = IncidentYear)) +
  geom_boxplot()

##QQ plot
car::qqPlot(EDAdata_outliers$IncidentYear)

## [1]  4 40
#histogram distribution
year_hist <- EDAdata_outliers %>% 
               ggplot2::ggplot(aes(x=IncidentYear)) + 
                        geom_bar(fill="#482878") + 
                        scale_x_continuous(breaks = seq(2012, 2021, by = 1)) +
                        scale_y_continuous(expand = c(0, 0), limits = c(0, 125)) +
                        geom_text(stat='count', aes(label=..count..), vjust=-1) +
                        labs(x = "Incident Year",
                             y = "Number of Declarations",
                             title = "Disaster Declarations per Year (2012 - 2021)")
year_hist

#save file
year_fig_file = here("results","incidentyear.png")
ggsave(filename = year_fig_file, plot = year_hist)
## Saving 7 x 5 in image
#examine graphical relationship with outcomes of interest
#look at total cost per incident year
req_fig_year <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = IncidentYear)) +
                      geom_bar(aes(y = ReqAmt),
                               stat = "identity",
                               fill = "#6ece58") +
                      scale_y_continuous(expand = c(0, 0), 
                                         labels = scales::dollar_format()) +
                      scale_x_continuous(breaks = seq(2012, 2021, by = 1)) +
                      labs(x = "Incident Year",
                           y = "Funds ($)",
                           subtitle = "Requested")
req_fig_year

#repeat for obligated funds
obl_fig_year <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = IncidentYear)) +
                      geom_bar(aes(y = OblAmt),
                               stat = "identity",
                               fill = "#26828e") +
                      scale_y_continuous(expand = c(0, 0),
                                         labels = scales::dollar_format()) +
                      scale_x_continuous(breaks = seq(2012, 2021, by = 1)) +
                      labs(x = "Incident Year",
                           y = "Funds ($)",
                           subtitle = "Obligated")
obl_fig_year

#combine into one plot
plot_col <- cowplot::plot_grid(req_fig_year, 
                                     obl_fig_year,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
plot_col

title1 <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs Obligated FEMA Funds Per Year",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme( plot.margin = margin(0, 0, 0, 7))

funds_fig_year <- cowplot::plot_grid(
                              title1, plot_col,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
funds_fig_year

#save file
funds_year_fig_file = here("results","funds-years.png")
ggsave(filename = funds_year_fig_file, plot = funds_fig_year)
## Saving 7 x 5 in image
#create a table where columns represent requested and obligated funds and rows are 5 most disaster-prone states
decs_year_3 <- EDAdata_outliers %>%
                    dplyr::add_count(IncidentYear) %>%
                    dplyr::filter(dense_rank(-n) < 4) %>%
                    gtsummary::select(IncidentYear, ReqAmt, OblAmt) %>%
                    gtsummary::tbl_summary(
                      by = IncidentYear,
                      label = list(IncidentYear ~ "Year",
                                   ReqAmt ~ "Requested Funds",
                                   OblAmt ~ "Obligated Funds"),
                      statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
                    gtsummary::modify_header(label ~ "**Variable**") %>% 
                    gtsummary::modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Year**") %>%
                    gtsummary::modify_caption("**Table 2. FEMA Funding for the 3 Costliest Years (2012 - 2021)**") %>%
                    gtsummary::bold_labels()
decs_year_3
Table 2. FEMA Funding for the 3 Costliest Years (2012 - 2021)
Variable Year
2012, N = 331 2017, N = 361 2020, N = 851
Requested Funds 11,106,321 (55,959,108) 155,310,630 (735,733,161) 74,188,913 (115,267,136)
Obligated Funds 11,388,973 (56,050,414) 155,887,178 (739,000,529) 74,194,235 (115,269,357)

1 Mean (SD)

#save file
decs_year_file = here("results", "decs-year-3.Rds")
saveRDS(decs_year_3, file = decs_year_file)

Incident Month

#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$IncidentMonth, report.nas = FALSE)
## Frequencies  
## EDAdata_outliers$IncidentMonth  
## Label: Declaration Month  
## Type: Ordered Factor  
## 
##                   Freq        %   % Cum.
## --------------- ------ -------- --------
##         January     71    21.98    21.98
##        February     22     6.81    28.79
##           March     22     6.81    35.60
##           April     24     7.43    43.03
##             May     22     6.81    49.85
##            June     25     7.74    57.59
##            July     13     4.02    61.61
##          August     31     9.60    71.21
##       September     30     9.29    80.50
##         October     41    12.69    93.19
##        November      8     2.48    95.67
##        December     14     4.33   100.00
##           Total    323   100.00   100.00
#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#filter top 5 states
#start with obligated amount
obl_fig_month <- EDAdata_outliers %>%
                    dplyr::add_count(state) %>%
                    dplyr::filter(dplyr::dense_rank(-n) < 5) %>%
                    ggplot2::ggplot(aes(x = IncidentMonth, y = OblAmt)) +
                      geom_boxplot(color = "#440154") +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      labs(x = "Incident Month",
                           y = "Funds ($)",
                           subtitle = "Obligated")
obl_fig_month

#repeat for requested funds
req_fig_month <- EDAdata_outliers %>%
                    dplyr::add_count(state) %>%
                    dplyr::filter(dplyr::dense_rank(-n) < 6) %>%
                    ggplot2::ggplot(aes(x = IncidentMonth, y = ReqAmt)) +
                      geom_boxplot(color = "#440154") +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      labs(x = " " ,
                           y = "Funds ($)",
                           subtitle = "Requested")
req_fig_month

#combine into one plot
plot_month <- cowplot::plot_grid(req_fig_month, 
                                     obl_fig_month,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
plot_month

title_month <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs Obligated FEMA Funds By Month",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme(plot.margin = margin(0, 0, 0, 7))

plot_month_funds <- cowplot::plot_grid(
                              title_month, plot_month,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
plot_month_funds

#save file
month_fig_file = here("results","month-funds.png")
ggsave(filename = month_fig_file, plot = plot_month_funds)
## Saving 7 x 5 in image
#create a table where columns represent requested and obligated funds and rows are 5 most disaster-prone states
decs_month_5 <- EDAdata_outliers %>%
                    dplyr::add_count(IncidentMonth) %>%
                    dplyr::filter(dense_rank(-n) < 6) %>%
                    gtsummary::select(IncidentMonth, ReqAmt, OblAmt) %>%
                    gtsummary::tbl_summary(
                      by = IncidentMonth,
                      label = list(IncidentMonth ~ "Incident Month",
                                   ReqAmt ~ "Requested Funds",
                                   OblAmt ~ "Obligated Funds"),
                      statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
                    gtsummary::modify_header(label ~ "**Variable**") %>% 
                    gtsummary::modify_spanning_header(c("stat_1", "stat_2", "stat_3", "stat_4", "stat_5") ~ "**Incident Month**") %>%
                    gtsummary::modify_caption("**Table 4. FEMA Funding by Month 2012 - 2021**") %>%
                    gtsummary::bold_labels()
decs_month_5
Table 4. FEMA Funding by Month 2012 - 2021
Variable Incident Month June, N = 251 July, N = 01 August, N = 311 September, N = 301 October, N = 411 November, N = 01 December, N = 01
January, N = 711 February, N = 01 March, N = 01 April, N = 01 May, N = 01
Requested Funds 86,384,351 (122,106,804) NA (NA) NA (NA) NA (NA) NA (NA) 306,004 (458,082) NA (NA) 28,337,301 (83,202,807) 157,997,671 (799,948,320) 30,303,760 (115,811,181) NA (NA) NA (NA)
Obligated Funds 86,397,557 (122,103,801) NA (NA) NA (NA) NA (NA) NA (NA) 306,434 (459,213) NA (NA) 28,349,776 (83,236,236) 158,657,132 (803,575,067) 30,531,625 (115,807,818) NA (NA) NA (NA)

1 Mean (SD)

#save file
decs_month_file = here("results", "decs-month-5.Rds")
saveRDS(decs_month_5, file = decs_month_file)

Predictor: Declaration Type

#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$declarationType, report.nas = FALSE)
## Frequencies  
## EDAdata_outliers$declarationType  
## Label: Declaration Type  
## Type: Character  
## 
##               Freq        %   % Cum.
## ----------- ------ -------- --------
##          DR    260    80.50    80.50
##          EM     63    19.50   100.00
##       Total    323   100.00   100.00
#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_DT <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = declarationType, y = OblAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      scale_x_discrete(labels = c("Major Disaster Declaration", "Emergency Declaration")) + 
                      labs(x = "Declaration Type",
                           y = "Funds ($)",
                           subtitle = "Obligated")
obl_fig_DT

#repeat for requested funds
req_fig_DT <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = declarationType, y = ReqAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      scale_x_discrete(labels = c("Major Disaster Declaration", "Emergency Declaration")) + 
                      labs(x = " ",
                           y = "Funds ($)",
                           subtitle = "Requested")
req_fig_DT

#combine into one plot
plot_DT <- cowplot::plot_grid(req_fig_DT, 
                                     obl_fig_DT,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
plot_DT

title_DT <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs Obligated FEMA Funds By Declaration Type",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme(plot.margin = margin(0, 0, 0, 7))

plot_DT_funds <- cowplot::plot_grid(
                              title_DT, plot_DT,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
plot_DT_funds

#save file
DT_fig_file = here("results","DT-funds.png")
ggsave(filename = DT_fig_file, plot = plot_DT_funds)
## Saving 7 x 5 in image
#create a table where rows represent requested and obligated funds and columns are declaration type
decs_dtype <- EDAdata_outliers %>%
                    gtsummary::select(declarationType, ReqAmt, OblAmt) %>%
                    gtsummary::tbl_summary(
                      by = declarationType,
                      label = list(declarationType ~ "Type",
                                   ReqAmt ~ "Requested Funds",
                                   OblAmt ~ "Obligated Funds"),
                      statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
                    gtsummary::modify_header(label ~ "**Variable**") %>% 
                    gtsummary::modify_header(update = list(
                                               stat_1 ~ "**Major Disaster**",
                                               stat_2 ~ "**Emergency**")) %>% 
                    gtsummary::modify_spanning_header(c("stat_1", "stat_2") ~ "**Type**") %>%
                    gtsummary::modify_caption("**Table 4. FEMA Funding for Declaration Types 2012 - 2021**") %>%
                    gtsummary::bold_labels()
decs_dtype
Table 4. FEMA Funding for Declaration Types 2012 - 2021
Variable Type
Major Disaster1 Emergency1
Requested Funds 50,782,764 (284,903,397) 518,576 (752,681)
Obligated Funds 50,903,565 (286,070,333) 518,576 (752,681)

1 Mean (SD)

#save file
decs_dtype_file = here("results", "decs-DT.Rds")
saveRDS(decs_dtype, file = decs_dtype_file)

Predictor: Incident Type

#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$incidentType, report.nas = FALSE)
## Frequencies  
## EDAdata_outliers$incidentType  
## Label: Incident Type  
## Type: Character  
## 
##                          Freq        %   % Cum.
## ---------------------- ------ -------- --------
##             Biological     60    18.58    18.58
##             Earthquake      4     1.24    19.81
##                   Fire     20     6.19    26.01
##                  Flood     61    18.89    44.89
##              Hurricane     71    21.98    66.87
##                  Other     14     4.33    71.21
##       Severe Ice Storm      4     1.24    72.45
##        Severe Storm(s)     82    25.39    97.83
##                Tornado      7     2.17   100.00
##                  Total    323   100.00   100.00
#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_IT <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = incidentType, y = OblAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      labs(x = "Incident Type",
                           y = "Funds ($)",
                           subtitle = "Obligated") +
                      theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
obl_fig_IT

#repeat for requested funds
req_fig_IT <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = incidentType, y = ReqAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
                      scale_y_continuous(labels = scales::dollar_format()) + 
                      labs(x = " ",
                           y = "Funds ($)",
                           subtitle = "Requested") +
                      theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
req_fig_IT

#combine into one plot
plot_IT <- cowplot::plot_grid(req_fig_IT, 
                                     obl_fig_IT,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
plot_IT

title_IT <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs Obligated FEMA Funds By Incident Type",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme(plot.margin = margin(0, 0, 0, 7))

plot_IT_funds <- cowplot::plot_grid(
                              title_IT, plot_IT,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
plot_IT_funds

#save file
IT_fig_file = here("results","IT-funds.png")
ggsave(filename = IT_fig_file, plot = plot_IT_funds)
## Saving 7 x 5 in image
#create a table where rows represent requested and obligated funds and columns are declaration type
decs_itype3 <- EDAdata %>%
                    dplyr::add_count(incidentType) %>%
                    dplyr::filter(dense_rank(-n) < 4) %>%
                    gtsummary::select(incidentType, ReqAmt, OblAmt) %>%
                    gtsummary::tbl_summary(
                      by = incidentType,
                      label = list(incidentType ~ "Type",
                                   ReqAmt ~ "Requested Funds",
                                   OblAmt ~ "Obligated Funds"),
                      statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
                    gtsummary::modify_header(label ~ "**Variable**") %>% 
                    gtsummary::modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Type**") %>%
                    gtsummary::modify_caption("**Table 4. FEMA Funding for Declaration Types 2012 - 2021**") %>%
                    gtsummary::bold_labels()
decs_itype3
Table 4. FEMA Funding for Declaration Types 2012 - 2021
Variable Type
Flood, N = 611 Hurricane, N = 731 Severe Storm(s), N = 821
Requested Funds 2,676,879 (11,264,643) 82,137,002 (515,877,545) 412,896 (1,056,124)
Obligated Funds 2,691,729 (11,268,277) 82,541,085 (518,171,447) 413,710 (1,057,092)

1 Mean (SD)

#save file
decs_itype3_file = here("results", "decs-IT-3.Rds")
saveRDS(decs_itype3, file = decs_itype3_file)

Predictor: Incident Duration

#convert class to numeric
EDAdata_outliers$IncidentDuration <- as.numeric(EDAdata_outliers$IncidentDuration, units="days")

#summary statistics since it's continuous
base::summary(EDAdata_outliers$IncidentDuration)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0     5.0    14.0   136.9    47.5   660.0
#NAs are ongoing events

#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = IncidentDuration)) +
  geom_density()

#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = IncidentDuration)) +
  geom_boxplot()

##QQ plot
car::qqPlot(EDAdata_outliers$IncidentDuration)

## [1] 2 5
#examine graphical relationship with outcomes of interest
#look at total cost per incident month
req_fig_dur <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = IncidentDuration)) +
                      geom_point(aes(y = ReqAmt),
                                 color = "#6ece58",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = "Incident Duration (Days)",
                           y = "Funds ($)",
                           subtitle = "Requested")
req_fig_dur
## Warning: Removed 1 rows containing missing values (geom_point).

#repeat for obligated funds
obl_fig_dur <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = IncidentDuration)) +
                      geom_point(aes(y = OblAmt),
                                 color = "#26828e",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = "Incident Duration (Days)",
                           y = "Funds ($)",
                           subtitle = "Obligated")
obl_fig_dur
## Warning: Removed 1 rows containing missing values (geom_point).

#combine into one plot
plot_col2 <- cowplot::plot_grid(req_fig_dur, 
                                     obl_fig_dur,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).
plot_col2

title3 <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs Obligated FEMA Funds by Incident Duration (Under $1B)",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme( plot.margin = margin(0, 0, 0, 7))

funds_fig_dur <- cowplot::plot_grid(
                              title3, plot_col2,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
funds_fig_dur

#save file
funds_dur_fig_file = here("results","funds-dur.png")
ggsave(filename = funds_dur_fig_file, plot = funds_fig_dur)
## Saving 7 x 5 in image

Predictor: Population

#convert class to numeric
EDAdata_outliers$Population <- as.numeric(gsub(",", "", EDAdata_outliers$Population))

#summary statistics since it's continuous
base::summary(EDAdata_outliers$Population)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   576851  3011524  5706494  9242866 10439388 39538223
#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = Population)) +
  geom_density()

#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = Population)) +
  geom_boxplot()

##QQ plot
car::qqPlot(EDAdata_outliers$Population)

## [1] 20 21
#examine graphical relationship with outcomes of interest
#look at total cost per pop
req_fig_pop <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = Population)) +
                      geom_point(aes(y = ReqAmt),
                                 color = "#6ece58",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      scale_x_continuous(label = comma,
                                         limits = c(0, 40000000)) +
                      labs(x = "State Population",
                           y = "Funds ($)",
                           subtitle = "Requested")
req_fig_pop
## Warning: Removed 1 rows containing missing values (geom_point).

#repeat for obligated funds
obl_fig_pop <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = Population)) +
                      geom_point(aes(y = OblAmt),
                                 color = "#26828e",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      scale_x_continuous(label = comma,
                                         limits = c(0, 40000000)) +
                      labs(x = "State Population",
                           y = "Funds ($)",
                           subtitle = "Obligated")
obl_fig_pop
## Warning: Removed 1 rows containing missing values (geom_point).

#combine into one plot
plot_col3 <- cowplot::plot_grid(req_fig_pop, 
                                     obl_fig_pop,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).
plot_col3

title4 <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs obligated FEMA funds by State Population (Under $1B)",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme( plot.margin = margin(0, 0, 0, 7))

funds_fig_pop <- cowplot::plot_grid(
                              title4, plot_col3,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
funds_fig_pop

#save file
funds_pop_fig_file = here("results","funds-pop.png")
ggsave(filename = funds_pop_fig_file, plot = funds_fig_pop)
## Saving 7 x 5 in image

Predictor: Total Number of Agencies Involved

#summary statistics since it's continuous
base::summary(EDAdata_outliers$TotalAgencies)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   2.000   3.000   6.245   8.000  55.000
#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = TotalAgencies)) +
  geom_density()

#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = TotalAgencies)) +
  geom_boxplot()

##QQ plot
car::qqPlot(EDAdata_outliers$TotalAgencies)

## [1] 233 275
#examine graphical relationship with outcomes of interest
#look at total cost per incident month
req_fig_ag <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = TotalAgencies)) +
                      geom_point(aes(y = ReqAmt),
                                 color = "#6ece58",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = "Number of Federal Agencies",
                           y = "Funds ($)",
                           subtitle = "Requested")
req_fig_ag
## Warning: Removed 1 rows containing missing values (geom_point).

#repeat for obligated funds
obl_fig_ag <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = TotalAgencies)) +
                      geom_point(aes(y = OblAmt),
                                 color = "#26828e",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = "Number of Federal Agencies",
                           y = "Funds ($)",
                           subtitle = "Obligated")
obl_fig_ag
## Warning: Removed 1 rows containing missing values (geom_point).

#combine into one plot
plot_col4 <- cowplot::plot_grid(req_fig_ag, 
                                     obl_fig_ag,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).
plot_col4

title5 <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs obligated FEMA funds by number of federal agencies (Under $1B)",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme( plot.margin = margin(0, 0, 0, 7))

funds_fig_ag <- cowplot::plot_grid(
                              title5, plot_col4,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
funds_fig_ag

#save file
funds_ag_fig_file = here("results","funds-ag.png")
ggsave(filename = funds_ag_fig_file, plot = funds_fig_ag)
## Saving 7 x 5 in image

Predictor: Number of Counties

#summary statistics since it's continuous
base::summary(EDAdata_outliers$Counties)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.00   46.00   67.00   74.96   88.00  254.00
#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = Counties)) +
  geom_density()

#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = Counties)) +
  geom_boxplot()

##QQ plot
car::qqPlot(EDAdata_outliers$Counties)

## [1] 266 267
#examine graphical relationship with outcomes of interest
#look at total cost per incident month
req_fig_count <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = Counties)) +
                      geom_point(aes(y = ReqAmt),
                                 color = "#6ece58",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = "Number of Counties",
                           y = "Funds ($)",
                           subtitle = "Requested")
req_fig_count
## Warning: Removed 1 rows containing missing values (geom_point).

#repeat for obligated funds
obl_fig_count <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = Counties)) +
                      geom_point(aes(y = OblAmt),
                                 color = "#26828e",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = "Number of Counties",
                           y = "Funds ($)",
                           subtitle = "Obligated")
obl_fig_count
## Warning: Removed 1 rows containing missing values (geom_point).

#combine into one plot
plot_col5 <- cowplot::plot_grid(req_fig_count, 
                                     obl_fig_count,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).
plot_col5

title6 <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs obligated FEMA funds by number of counties (Under $1B)",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme( plot.margin = margin(0, 0, 0, 7))

funds_fig_count <- cowplot::plot_grid(
                              title6, plot_col5,
                              ncol = 1,
                              rel_heights = c(0.1, 1))
funds_fig_count

#save file
funds_count_fig_file = here("results","funds-count.png")
ggsave(filename = funds_count_fig_file, plot = funds_fig_count)
## Saving 7 x 5 in image

Predictor: FEMA Cost Share Average

#summary statistics since it's continuous
base::summary(EDAdata_outliers$FEMACSAvg)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.6750  0.9458  1.0000  0.9641  1.0000  1.0000
#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = FEMACSAvg)) +
  geom_density()

#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = FEMACSAvg)) +
  geom_boxplot()

##QQ plot
car::qqPlot(EDAdata_outliers$FEMACSAvg)

## [1] 290  44
#examine graphical relationship with outcomes of interest
#look at total cost per incident month
req_fig_cs <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = FEMACSAvg)) +
                      geom_point(aes(y = ReqAmt),
                                 color = "#6ece58",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = "Average FEMA Cost Share",
                           y = "Funds ($)",
                           subtitle = "Requested")
req_fig_cs
## Warning: Removed 1 rows containing missing values (geom_point).

#repeat for obligated funds
obl_fig_cs <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = FEMACSAvg)) +
                      geom_point(aes(y = OblAmt),
                                 color = "#26828e",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = "Average FEMA Cost Share",
                           y = "Funds ($)",
                           subtitle = "Obligated")
obl_fig_cs
## Warning: Removed 1 rows containing missing values (geom_point).

#combine into one plot
plot_col6 <- cowplot::plot_grid(req_fig_cs, 
                                     obl_fig_cs,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).
plot_col6

title7 <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs obligated FEMA funds by avg FEMA cost share (Under $1B)",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme( plot.margin = margin(0, 0, 0, 7))

funds_fig_cs <- cowplot::plot_grid(
                              title7, plot_col6,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
funds_fig_cs

#save file
funds_cs_fig_file = here("results","funds-cs.png")
ggsave(filename = funds_cs_fig_file, plot = funds_fig_cs)
## Saving 7 x 5 in image

Predictor: IH Program

#convert to factor
EDAdata_outliers$IH <- as.factor(EDAdata_outliers$IH)

#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$IH, report.nas = FALSE)
## Frequencies  
## EDAdata_outliers$IH  
## Label: Individuals/Households Program Awarded  
## Type: Factor  
## 
##                     Freq        %   % Cum.
## ----------------- ------ -------- --------
##       Not Awarded    160    49.54    49.54
##           Awarded    163    50.46   100.00
##             Total    323   100.00   100.00
#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_IH <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = IH, y = OblAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
                      labs(x = "IH Program",
                           y = "Funds ($)",
                           subtitle = "Obligated") 
obl_fig_IH

#repeat for requested funds
req_fig_IH <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = IH, y = ReqAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
                      scale_y_continuous(labels = scales::dollar_format()) + 
                      scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
                      labs(x = " ",
                           y = "Funds ($)",
                           subtitle = "Requested") 
req_fig_IH

#combine into one plot
plot_IH <- cowplot::plot_grid(req_fig_IH, 
                                     obl_fig_IH,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
plot_IH

title_IH <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs Obligated FEMA Funds By IH Program Awardance",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme(plot.margin = margin(0, 0, 0, 7))

plot_IH_funds <- cowplot::plot_grid(
                              title_IH, plot_IH,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
plot_IH_funds

#save file
IH_fig_file = here("results","IH-funds.png")
ggsave(filename = IH_fig_file, plot = plot_IH_funds)
## Saving 7 x 5 in image
#create a table where rows represent requested and obligated funds and columns are declaration type
decs_ih <- EDAdata_outliers %>%
                    gtsummary::select(IH, ReqAmt, OblAmt) %>%
                    gtsummary::tbl_summary(
                      by = IH,
                      label = list(IH ~ "Individuals / Households Program",
                                   ReqAmt ~ "Requested Funds",
                                   OblAmt ~ "Obligated Funds"),
                      statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
                    gtsummary::modify_header(label ~ "**Variable**") %>% 
                    gtsummary::modify_header(update = list(
                                               stat_1 ~ "**Not Awarded**",
                                               stat_2 ~ "**Awarded**")) %>% 
                    gtsummary::modify_spanning_header(c("stat_1", "stat_2") ~ "**Individuals/Households Program**") %>%
                    gtsummary::modify_caption("**Table 5. FEMA Funding by IH Program Awardance 2012 - 2021**") %>%
                    gtsummary::bold_labels()
decs_ih
Table 5. FEMA Funding by IH Program Awardance 2012 - 2021
Variable Individuals/Households Program
Not Awarded1 Awarded1
Requested Funds 499,887 (956,929) 80,712,926 (356,868,389)
Obligated Funds 499,887 (956,929) 80,905,614 (358,341,553)

1 Mean (SD)

#save file
decs_ih_file = here("results", "decs-IH.Rds")
saveRDS(decs_ih, file = decs_ih_file)

Predictor: PA Program

#convert to factor
EDAdata_outliers$PA <- as.factor(EDAdata_outliers$PA)

#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$PA, report.nas = FALSE)
## Frequencies  
## EDAdata_outliers$PA  
## Label: Public Assistance Program Awarded  
## Type: Factor  
## 
##                     Freq        %   % Cum.
## ----------------- ------ -------- --------
##       Not Awarded      8     2.48     2.48
##           Awarded    315    97.52   100.00
##             Total    323   100.00   100.00
#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_PA <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = PA, y = OblAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
                      labs(x = "PA Program",
                           y = "Funds ($)",
                           subtitle = "FEMA funds obligated when PA program is awarded") 
obl_fig_PA

#repeat for requested funds
req_fig_PA <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = PA, y = ReqAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
                      scale_y_continuous(labels = scales::dollar_format()) + 
                      scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
                      labs(x = " ",
                           y = "Funds ($)",
                           title = "Requested") +
                      theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
req_fig_PA

#combine into one plot
plot_PA <- cowplot::plot_grid(req_fig_PA, 
                                     obl_fig_PA,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
plot_PA

title_PA <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs Obligated FEMA Funds By PA Program Awardance",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme(plot.margin = margin(0, 0, 0, 7))

plot_PA_funds <- cowplot::plot_grid(
                              title_PA, plot_PA,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
plot_PA_funds

#save file
PA_fig_file = here("results","PA-funds.png")
ggsave(filename = PA_fig_file, plot = plot_PA_funds)
## Saving 7 x 5 in image
#create a table where rows represent requested and obligated funds and columns are declaration type
decs_pa <- EDAdata_outliers %>%
                    gtsummary::select(PA, ReqAmt, OblAmt) %>%
                    gtsummary::tbl_summary(
                      by = PA,
                      label = list(PA ~ "Public Assistance Program",
                                   ReqAmt ~ "Requested Funds",
                                   OblAmt ~ "Obligated Funds"),
                      statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
                    gtsummary::modify_header(label ~ "**Variable**") %>% 
                    gtsummary::modify_header(update = list(
                                               stat_1 ~ "**Not Awarded**",
                                               stat_2 ~ "**Awarded**")) %>% 
                    gtsummary::modify_spanning_header(c("stat_1", "stat_2") ~ "**Public Assistance Program**") %>%
                    gtsummary::modify_caption("**Table 6. FEMA Funding by PA Program Awardance 2012 - 2021**") %>%
                    gtsummary::bold_labels()
decs_pa
Table 6. FEMA Funding by PA Program Awardance 2012 - 2021
Variable Public Assistance Program
Not Awarded1 Awarded1
Requested Funds 243,522 (419,425) 42,013,463 (259,455,341)
Obligated Funds 243,522 (419,425) 42,113,171 (260,515,668)

1 Mean (SD)

#save file
decs_pa_file = here("results", "decs-PA.Rds")
saveRDS(decs_pa, file = decs_pa_file)

Predictor: HM Program

#convert to factor
EDAdata_outliers$HM <- as.factor(EDAdata_outliers$HM)

#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$HM, report.nas = FALSE)
## Frequencies  
## EDAdata_outliers$HM  
## Label: Hazard Mitigation Program  
## Type: Factor  
## 
##                     Freq        %   % Cum.
## ----------------- ------ -------- --------
##       Not Awarded     64    19.81    19.81
##           Awarded    259    80.19   100.00
##             Total    323   100.00   100.00
#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_HM <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = HM, y = OblAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
                      scale_y_continuous(labels = scales::dollar_format()) +
                      scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
                      labs(x = "HM Program",
                           y = "Funds ($)",
                           subtitle = "Obligated") 
obl_fig_HM

#repeat for requested funds
req_fig_HM <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = HM, y = ReqAmt)) +
                      geom_boxplot() +
                      geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
                      scale_y_continuous(labels = scales::dollar_format()) + 
                      scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
                      labs(x = " ",
                           y = "Funds ($)",
                           subtitle = "Requested") 
req_fig_HM

#combine into one plot
plot_HM <- cowplot::plot_grid(req_fig_HM, 
                                     obl_fig_HM,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
plot_HM

title_HM <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs Obligated FEMA Funds By HM Program Awardance",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme(plot.margin = margin(0, 0, 0, 7))

plot_HM_funds <- cowplot::plot_grid(
                              title_HM, plot_HM,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
plot_HM_funds

#save file
PA_fig_file = here("results","PA-funds.png")
ggsave(filename = PA_fig_file, plot = plot_PA_funds)
## Saving 7 x 5 in image
#create a table where rows represent requested and obligated funds and columns are declaration type
decs_hm <- EDAdata %>%
                    gtsummary::select(HM, ReqAmt, OblAmt) %>%
                    gtsummary::tbl_summary(
                      by = HM,
                      label = list(HM ~ "Hazard Mitigation Program",
                                   ReqAmt ~ "Requested Funds",
                                   OblAmt ~ "Obligated Funds"),
                      statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
                    gtsummary::modify_header(label ~ "**Variable**") %>% 
                    gtsummary::modify_header(update = list(
                                               stat_1 ~ "**Not Awarded**",
                                               stat_2 ~ "**Awarded**")) %>% 
                    gtsummary::modify_spanning_header(c("stat_1", "stat_2") ~ "**Hazard Mitigation Program**") %>%
                    gtsummary::modify_caption("**Table 7. FEMA Funding by HM Program Awardance 2012 - 2021**") %>%
                    gtsummary::bold_labels()
decs_hm
Table 7. FEMA Funding by HM Program Awardance 2012 - 2021
Variable Hazard Mitigation Program
Not Awarded1 Awarded1
Requested Funds 495,237 (742,966) 50,782,705 (284,903,408)
Obligated Funds 495,237 (742,966) 50,903,506 (286,070,343)

1 Mean (SD)

#save file
decs_hm_file = here("results", "decs-HM.Rds")
saveRDS(decs_hm, file = decs_hm_file)

Predictor: Response Duration

#summary statistics since it's continuous
base::summary(EDAdata_outliers$ResponseDays)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   70.04  660.00 1181.04 1404.26 2061.02 3588.00
#NAs are ongoing events

#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = ResponseDays)) +
  geom_density()

#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = ResponseDays)) +
  geom_boxplot()

##QQ plot
car::qqPlot(EDAdata_outliers$ResponseDays)

## [1] 298 100
#examine graphical relationship with outcomes of interest
req_fig_resp <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = ResponseDays)) +
                      geom_point(aes(y = ReqAmt),
                                 color = "#6ece58",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = " ",
                           y = "Funds ($)",
                           subtitle = "Requested")
req_fig_resp
## Warning: Removed 1 rows containing missing values (geom_point).

#repeat for obligated funds
obl_fig_resp <- EDAdata_outliers %>%
                    ggplot2::ggplot(aes(x = ResponseDays)) +
                      geom_point(aes(y = OblAmt),
                                 color = "#26828e",
                                 show.legend = FALSE) +
                      scale_y_continuous(expand = c(0, 0),
                                         limits = c(0, 1000000000),
                                         labels = scales::dollar_format()) +
                      labs(x = "Response Duration (Days)",
                           y = "Funds ($)",
                           subtitle = "Obligated")
obl_fig_resp
## Warning: Removed 1 rows containing missing values (geom_point).

#combine into one plot
plot_col7 <- cowplot::plot_grid(req_fig_resp, 
                                     obl_fig_resp,
                                     ncol = 1,
                                     align = "v",
                                     rel_heights = c(0.5,0.5))
## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).
plot_col7

title8 <- cowplot::ggdraw() + 
                    draw_label(
                      "Requested vs obligated FEMA funds by response duration (Under $1B)",
                      fontface = 'bold',
                      x = 0,
                      hjust = 0) +
                    theme( plot.margin = margin(0, 0, 0, 7))

funds_fig_resp <- cowplot::plot_grid(
                              title8, plot_col7,
                              ncol = 1,
                              # rel_heights values control vertical title margins
                              rel_heights = c(0.1, 1))
funds_fig_resp

#save file
funds_resp_fig_file = here("results","funds-resp.png")
ggsave(filename = funds_resp_fig_file, plot = funds_fig_resp)
## Saving 7 x 5 in image

Create “Table 1”

Make the summary table for the manuscript. Variables to include:

#specify units
table1::units(EDAdata$IncidentDuration) <- "days"
table1::units(EDAdata$ResponseDays) <- "days"

#create table
table1 <- table1::table1(~ ReqAmt + OblAmt + FEMACSAvg + TotalAgencies + IH_pct + PA_pct + HM_pct + ResponseDays +
                           IncidentDuration + IncidentMonth + IncidentYear + incidentType + declarationType +
                           FEMARegion + Counties + Population, data = EDAdata)
table1
Overall
(N=326)
Requested FEMA Funds
Mean (SD) 40600000 (255000000)
Median [Min, Max] 420000 [0.000000000000455, 4390000000]
Obligated FEMA Funds
Mean (SD) 40700000 (256000000)
Median [Min, Max] 434000 [0.000000000000455, 4410000000]
Average FEMA Cost Share
Mean (SD) 0.964 (0.0573)
Median [Min, Max] 1.00 [0.675, 1.00]
Federal Agencies Involved
Mean (SD) 6.23 (7.03)
Median [Min, Max] 3.00 [1.00, 55.0]
Percent of Counties Awarded IH
Mean (SD) 0.338 (1.59)
Median [Min, Max] 0.00197 [0, 27.7]
Percent of Counties Awarded PA
Mean (SD) 0.594 (1.56)
Median [Min, Max] 0.357 [0, 27.3]
Percent of Counties Awarded HM
Mean (SD) 0.476 (1.56)
Median [Min, Max] 0.207 [0, 27.3]
Response Duration (days)
Mean (SD) 1400 (898)
Median [Min, Max] 1180 [70.0, 3590]
Incident Duration (days)
Mean (SD) 136 (250)
Median [Min, Max] 14.0 [0, 660]
Declaration Month
January 71 (21.8%)
February 22 (6.7%)
March 22 (6.7%)
April 24 (7.4%)
May 22 (6.7%)
June 25 (7.7%)
July 13 (4.0%)
August 32 (9.8%)
September 31 (9.5%)
October 41 (12.6%)
November 9 (2.8%)
December 14 (4.3%)
Incident Year
Mean (SD) 2020 (2.89)
Median [Min, Max] 2020 [2010, 2020]
Incident Type
Biological 60 (18.4%)
Earthquake 4 (1.2%)
Fire 20 (6.1%)
Flood 61 (18.7%)
Hurricane 73 (22.4%)
Other 15 (4.6%)
Severe Ice Storm 4 (1.2%)
Severe Storm(s) 82 (25.2%)
Tornado 7 (2.1%)
Declaration Type
DR 261 (80.1%)
EM 65 (19.9%)
FEMA Region
Region I 27 (8.3%)
Region II 19 (5.8%)
Region III 25 (7.7%)
Region IV 62 (19.0%)
Region IX 31 (9.5%)
Region V 37 (11.3%)
Region VI 54 (16.6%)
Region VII 20 (6.1%)
Region VIII 24 (7.4%)
Region X 27 (8.3%)
Counties per State
Mean (SD) 75.3 (53.5)
Median [Min, Max] 67.0 [1.00, 254]
State Population
Mean (SD) 9310000 (10300000)
Median [Min, Max] 5710000 [577000, 39500000]
#save file
table1_file = here("results", "table1.Rds")
saveRDS(table1, file = table1_file)

Map Figure

Make the following four maps:

  1. FEMA Regions
  2. Density by total number of declarations
  3. Density by total requested funds
  4. Density by obligated funds
#pull geographic boundaries from maps package
MainStates <- map_data("state")

#fix capitalization issue in map data
MainStates$name <- str_to_title(MainStates$region)

#create summary df by state for EDAdata_analysis
#count number of declarations per state
EDA_sum_1 <- EDAdata_outliers %>%
                dplyr::group_by(name,
                                state,
                                latitude,
                                longitude,
                                Population,
                                Counties,
                                FEMARegion) %>%
                dplyr::count(state)

#find total of requested and obligated funds
EDA_sum_2 <- EDAdata_outliers %>%
                dplyr::group_by(name) %>%
                dplyr::summarize(RequestedAmt = sum(ReqAmt),
                                 ObligatedAmt = sum(OblAmt))

#combine the two
EDA_sums <- inner_join(EDA_sum_1, EDA_sum_2, by = "name")

#merge MainStates and EDAdata_outliers
MergedStates <- inner_join(MainStates, EDA_sums, by = "name")

#rename count variable
MapsData <- MergedStates %>%
                  dplyr::mutate(NumDecs = n) %>%
                  dplyr::select(-n)

#create map with FEMA Regions
map1 <- ggplot(MapsData, aes(x = long, 
                             y = lat, 
                             group = group)) +
      geom_polygon(aes(fill = FEMARegion), colour = alpha("white", 1 / 2), size = 0.2) +
      geom_polygon(data = MergedStates, colour = "white", fill = NA) +
      coord_fixed() +
      scale_fill_manual(values = c("#fde725", "#b5de2b", "#6ece58", "#35b779", "#1f9e89", "#26828e", "#31688e",
                                   "#3e4989", "#482878", "#440154")) +
      theme(axis.line = element_blank(), axis.text = element_blank(),
            axis.ticks = element_blank(), axis.title = element_blank())
map1

#save file
map1_fig_file = here("results","map1.png")
ggsave(filename = map1_fig_file, plot = map1)
## Saving 7 x 5 in image
#create map with number of declarations
map2 <- ggplot(MapsData, aes(x = long, 
                             y = lat, 
                             group = group)) +
      geom_polygon(aes(fill = NumDecs), colour = alpha("white", 1 / 2), size = 0.2) +
      geom_polygon(data = MergedStates, colour = "white", fill = NA) +
      coord_fixed() +
      scale_fill_viridis_c(option = "D",
                           name = "Number of Declarations") +
      theme(axis.line = element_blank(), axis.text = element_blank(),
            axis.ticks = element_blank(), axis.title = element_blank())
map2

#save file
map2_fig_file = here("results","map2.png")
ggsave(filename = map2_fig_file, plot = map2)
## Saving 7 x 5 in image
#create map with requested amount
map3 <- ggplot(MapsData, aes(x = long, 
                             y = lat, 
                             group = group)) +
      geom_polygon(aes(fill = RequestedAmt), colour = alpha("white", 1 / 2), size = 0.2) +
      geom_polygon(data = MergedStates, colour = "white", fill = NA) +
      coord_fixed() +
      scale_fill_viridis_c(option = "D",
                           labels=scales::dollar_format(),
                           name = "Requested Amount") +
      theme(axis.line = element_blank(), axis.text = element_blank(),
            axis.ticks = element_blank(), axis.title = element_blank())
map3

#save file
map3_fig_file = here("results","map3.png")
ggsave(filename = map3_fig_file, plot = map3)
## Saving 7 x 5 in image
#create map with obligated amount
map4 <- ggplot(MapsData, aes(x = long, 
                             y = lat, 
                             group = group)) +
      geom_polygon(aes(fill = ObligatedAmt), colour = alpha("white", 1 / 2), size = 0.2) +
      geom_polygon(data = MergedStates, colour = "white", fill = NA) +
      coord_fixed() +
      scale_fill_viridis_c(option = "D",
                           labels=scales::dollar_format(),
                           name = "Obligated Amount") +
      theme(axis.line = element_blank(), axis.text = element_blank(),
            axis.ticks = element_blank(), axis.title = element_blank())
map4

#save file
map4_fig_file = here("results","map4.png")
ggsave(filename = map4_fig_file, plot = map4)
## Saving 7 x 5 in image